fix bug

.gitignore

@@ -1,8 +1,8 @@
-.venv/
-env/
-__pycache__/
-*.py[cod]
-.pytest_cache/
-.Python
 my_picocom_logfile.txt
-sample_data/
+*pyc
+__pycache__/
+*.log
+*.tmp
+*.bak
+*.swp
+*.swo       

README.md

@@ -1,185 +0,0 @@
-# RFG STM32 ADC Receiver GUI
-
-PyQtGraph-приложение для чтения свипов из последовательного порта и отображения:
-
-- текущего свипа
-- водопада по свипам
-- FFT текущего свипа
-- B-scan по FFT
-
-После рефакторинга проект разделен на пакет `rfg_adc_plotter`. Старый запуск через `RFG_ADC_dataplotter.py` сохранен как совместимый wrapper.
-
-## Структура
-
-- `RFG_ADC_dataplotter.py` — совместимый entrypoint
-- `rfg_adc_plotter/cli.py` — CLI-аргументы
-- `rfg_adc_plotter/io/` — чтение порта и парсеры протоколов
-- `rfg_adc_plotter/processing/` — FFT, нормировка, калибровка, поиск пиков
-- `rfg_adc_plotter/state/` — runtime state и кольцевые буферы
-- `rfg_adc_plotter/gui/pyqtgraph_backend.py` — GUI на PyQtGraph
-- `replay_pty.py` — воспроизведение захвата через виртуальный PTY
-
-## Зависимости
-
-Минимально нужны:
-
-```bash
-python3 -m venv .venv
-. .venv/bin/activate
-pip install numpy pyqtgraph PyQt5
-```
-
-Если `pyserial` не установлен, приложение попробует открыть порт через raw TTY.
-
-## Быстрый старт
-
-Запуск через старый entrypoint:
-
-```bash
-.venv/bin/python RFG_ADC_dataplotter.py /dev/ttyACM0
-```
-
-Запуск напрямую через пакет:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0
-```
-
-Показать справку:
-
-```bash
-.venv/bin/python RFG_ADC_dataplotter.py --help
-```
-
-## Примеры запуска
-
-Обычный запуск с живого порта:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --baud 115200
-```
-
-Больше истории в водопаде и ограничение FPS:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --max-sweeps 400 --max-fps 20
-```
-
-Фиксированный диапазон по оси Y:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --ylim -1000,1000
-```
-
-С включенной нормировкой `simple`:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --norm-type simple
-```
-
-Режим измерения ширины главного пика FFT:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --calibrate
-```
-
-Поиск топ-3 пиков относительно rolling median reference:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --peak_search --peak_ref_window 1.5
-```
-
-Вычитание среднего спектра по последним секундам:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --spec-mean-sec 3
-```
-
-## Протоколы ввода
-
-ASCII-протокол по умолчанию:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0
-```
-
-Legacy binary:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --bin
-```
-
-Logscale binary с парой `int32`:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --logscale
-```
-
-Logscale binary `16-bit x2`:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --parser_16_bit_x2
-```
-
-Тестовый парсер для экспериментального `16-bit x2` потока:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --parser_test
-```
-
-## Локальная проверка через replay_pty
-
-Если есть лог-файл захвата, его можно воспроизвести как виртуальный последовательный порт.
-
-В первом терминале:
-
-```bash
-.venv/bin/python replay_pty.py my_picocom_logfile.txt --pty /tmp/ttyVIRT0 --speed 1.0
-```
-
-Во втором терминале:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /tmp/ttyVIRT0
-```
-
-Максимально быстрый replay:
-
-```bash
-.venv/bin/python replay_pty.py my_picocom_logfile.txt --pty /tmp/ttyVIRT0 --speed 0
-```
-
-## Удаленный захват по SSH
-
-В приложении SSH-источник не встроен. Для удаленной проверки нужно сначала получить поток или лог на локальную машину, а затем либо:
-
-- запускать GUI напрямую на локальном PTY
-- сохранять поток в файл и воспроизводить его через `replay_pty.py`
-
-Пример команды для ручной диагностики удаленного устройства:
-
-```bash
-ssh 192.148.0.148 'ls -l /dev/ttyACM0'
-```
-
-Если на удаленной машине есть доступ к потоку, удобнее сохранять его в файл и уже этот файл гонять локально через `replay_pty.py`.
-
-## Проверка и тесты
-
-Синтаксическая проверка:
-
-```bash
-python3 -m compileall RFG_ADC_dataplotter.py replay_pty.py rfg_adc_plotter tests
-```
-
-Запуск тестов:
-
-```bash
-.venv/bin/python -m unittest discover -s tests -v
-```
-
-## Замечания
-
-- Поддерживается только PyQtGraph backend.
-- `--backend mpl` оставлен только для совместимости CLI и завершится ошибкой.
-- Каталоги `sample_data/` и локальные логи добавлены в `.gitignore` и не считаются частью обязательного tracked-состояния репозитория.

RFG_ADC_dataplotter.py

@@ -1,8 +0,0 @@
-#!/usr/bin/env python3
-"""Compatibility wrapper for the modularized ADC plotter."""
-
-from rfg_adc_plotter.main import main
-
-
-if __name__ == "__main__":
-    main()

replay_pty.py

@@ -1,7 +1,16 @@
 #!/usr/bin/env python3
-"""Replay a capture file through a pseudo-TTY for local GUI verification."""
+"""
+Эмулятор серийного порта: воспроизводит лог-файл в цикле через PTY.
 
-from __future__ import annotations
+Использование:
+  python3 replay_pty.py my_picocom_logfile.txt
+  python3 replay_pty.py my_picocom_logfile.txt --pty /tmp/ttyVIRT0
+  python3 replay_pty.py my_picocom_logfile.txt --speed 2.0   # в 2 раза быстрее реального
+  python3 replay_pty.py my_picocom_logfile.txt --speed 0     # максимально быстро
+
+Затем в другом терминале:
+  python -m rfg_adc_plotter.main /tmp/ttyVIRT0
+"""
 
 import argparse
 import os
@@ -9,7 +18,7 @@ import sys
 import time
 
 
-def main() -> None:
+def main():
     parser = argparse.ArgumentParser(
         description="Воспроизводит лог-файл через PTY как виртуальный серийный порт."
     )
@@ -34,18 +43,20 @@ def main() -> None:
         "--baud",
         type=int,
         default=115200,
-        help="Скорость (бод) для расчета задержек (по умолчанию 115200)",
+        help="Скорость (бод) для расчёта задержек (по умолчанию 115200)",
     )
     args = parser.parse_args()
 
     if not os.path.isfile(args.file):
         sys.stderr.write(f"[error] Файл не найден: {args.file}\n")
-        raise SystemExit(1)
+        sys.exit(1)
 
+    # Открываем PTY-пару: master (мы пишем) / slave (GUI читает)
     master_fd, slave_fd = os.openpty()
     slave_path = os.ttyname(slave_fd)
-    os.close(slave_fd)
+    os.close(slave_fd)  # GUI откроет slave сам по симлинку
 
+    # Симлинк с удобным именем
     try:
         os.unlink(args.pty)
     except FileNotFoundError:
@@ -53,30 +64,27 @@ def main() -> None:
     os.symlink(slave_path, args.pty)
 
     print(f"PTY slave : {slave_path}")
-    print(f"Симлинк   : {args.pty}  ->  {slave_path}")
-    print(f"Запустите : python3 -m rfg_adc_plotter.main {args.pty}")
+    print(f"Симлинк   : {args.pty}  →  {slave_path}")
+    print(f"Запустите : python -m rfg_adc_plotter.main {args.pty}")
     print("Ctrl+C для остановки.\n")
 
+    # Задержка на байт: 10 бит (8N1) / скорость / множитель
     if args.speed > 0:
         bytes_per_sec = args.baud / 10.0 * args.speed
         delay_per_byte = 1.0 / bytes_per_sec
     else:
         delay_per_byte = 0.0
 
-    chunk_size = 4096
     loop = 0
     try:
         while True:
             loop += 1
             print(f"[loop {loop}] {args.file}")
-            with open(args.file, "rb") as handle:
-                while True:
-                    chunk = handle.read(chunk_size)
-                    if not chunk:
-                        break
-                    os.write(master_fd, chunk)
+            with open(args.file, "rb") as f:
+                for line in f:
+                    os.write(master_fd, line)
                     if delay_per_byte > 0:
-                        time.sleep(delay_per_byte * len(chunk))
+                        time.sleep(delay_per_byte * len(line))
     except KeyboardInterrupt:
         print("\nОстановлено.")
     finally:

rfg_adc_plotter/__init__.py

@@ -1,3 +0,0 @@
-"""RFG ADC plotter package."""
-
-__all__ = []

rfg_adc_plotter/cli.py

@@ -1,120 +0,0 @@
-"""Command-line parser for the ADC plotter."""
-
-from __future__ import annotations
-
-import argparse
-
-
-def build_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(
-        description=(
-            "Читает свипы из виртуального COM-порта и рисует: "
-            "последний свип и водопад (реалтайм)."
-        )
-    )
-    parser.add_argument(
-        "port",
-        help="Путь к порту, например /dev/ttyACM1 или COM3 (COM10+: \\\\.\\COM10)",
-    )
-    parser.add_argument("--baud", type=int, default=115200, help="Скорость (по умолчанию 115200)")
-    parser.add_argument("--max-sweeps", type=int, default=200, help="Количество видимых свипов в водопаде")
-    parser.add_argument("--max-fps", type=float, default=30.0, help="Лимит частоты отрисовки, кадров/с")
-    parser.add_argument("--cmap", default="viridis", help="Цветовая карта водопада")
-    parser.add_argument(
-        "--spec-clip",
-        default="2,98",
-        help=(
-            "Процентильная обрезка уровней водопада спектров, %% (min,max). "
-            "Напр. 2,98. 'off' — отключить"
-        ),
-    )
-    parser.add_argument(
-        "--spec-mean-sec",
-        type=float,
-        default=0.0,
-        help=(
-            "Вычитание среднего по каждой частоте за последние N секунд "
-            "в водопаде спектров (0 — отключить)"
-        ),
-    )
-    parser.add_argument("--title", default="ADC Sweeps", help="Заголовок окна")
-    parser.add_argument(
-        "--fancy",
-        action="store_true",
-        help="Заполнять выпавшие точки средними значениями между соседними",
-    )
-    parser.add_argument(
-        "--ylim",
-        type=str,
-        default=None,
-        help="Фиксированные Y-пределы для кривой формата min,max (например -1000,1000). По умолчанию авто",
-    )
-    parser.add_argument(
-        "--backend",
-        choices=["auto", "pg", "mpl"],
-        default="pg",
-        help="Совместимый флаг. Поддерживаются только auto и pg; mpl удален.",
-    )
-    parser.add_argument(
-        "--norm-type",
-        choices=["projector", "simple"],
-        default="projector",
-        help="Тип нормировки: projector (по огибающим в [-1,+1]) или simple (raw/calib)",
-    )
-    parser.add_argument(
-        "--bin",
-        dest="bin_mode",
-        action="store_true",
-        help=(
-            "Бинарный протокол: старт свипа 0xFFFF,0xFFFF,0xFFFF,(CH<<8)|0x0A; "
-            "точки step,uint32(hi16,lo16),0x000A"
-        ),
-    )
-    parser.add_argument(
-        "--logscale",
-        action="store_true",
-        default=True,
-        help=(
-            "Новый бинарный протокол: точка несет пару int32 (avg_1, avg_2), "
-            "а свип считается как |10**(avg_1*0.001) - 10**(avg_2*0.001)|"
-        ),
-    )
-    parser.add_argument(
-        "--parser_16_bit_x2",
-        action="store_true",
-        help=(
-            "Бинарный logscale-протокол c парой int16 (avg_1, avg_2): "
-            "старт 0xFFFF,0xFFFF,0xFFFF,(CH<<8)|0x0A; точка step,avg1_lo16,avg2_lo16,0xFFFF"
-        ),
-    )
-    parser.add_argument(
-        "--parser_test",
-        action="store_true",
-        help=(
-            "Тестовый парсер для формата 16-bit x2: "
-            "одиночный 0xFFFF завершает точку, серия 0xFFFF начинает новый свип"
-        ),
-    )
-    parser.add_argument(
-        "--calibrate",
-        action="store_true",
-        help=(
-            "Режим измерения ширины главного пика FFT: рисует красные маркеры "
-            "границ и фона и выводит ширину пика в статус"
-        ),
-    )
-    parser.add_argument(
-        "--peak_search",
-        action="store_true",
-        help=(
-            "Поиск топ-3 пиков на FFT относительно референса (скользящая медиана) "
-            "с отрисовкой bounding box и параметров пиков"
-        ),
-    )
-    parser.add_argument(
-        "--peak_ref_window",
-        type=float,
-        default=1.0,
-        help="Ширина окна скользящей медианы для --peak_search, ГГц/м по оси FFT (по умолчанию 1.0)",
-    )
-    return parser

rfg_adc_plotter/constants.py

@@ -1,17 +1,13 @@
-"""Shared constants for sweep parsing and visualization."""
-
-WF_WIDTH = 1000
-FFT_LEN = 1024
-BACKGROUND_MEDIAN_SWEEPS = 64
-
-SWEEP_FREQ_MIN_GHZ = 3.3
-SWEEP_FREQ_MAX_GHZ = 14.3
-
-LOG_BASE = 10.0
-LOG_SCALER = 0.001
-LOG_POSTSCALER = 10.0
-LOG_EXP_LIMIT = 300.0
-
-C_M_S = 299_792_458.0
-
+WF_WIDTH = 1000  # максимальное число точек в ряду водопада
+FFT_LEN = 2048   # длина БПФ для спектра/водопада спектров
+# Порог для инверсии сырых данных: если среднее значение свипа ниже порога —
+# считаем, что сигнал «меньше нуля» и домножаем свип на -1
 DATA_INVERSION_THRESHOLD = 10.0
+
+# Параметры IFFT-спектра (временной профиль из спектра 3.2..14.3 ГГц)
+# Двусторонний спектр формируется как: [нули -14.3..-3.2 | нули -3.2..+3.2 | данные +3.2..+14.3]
+ZEROS_LOW = 758       # нули от -14.3 до -3.2 ГГц
+ZEROS_MID = 437       # нули от -3.2 до +3.2 ГГц
+SWEEP_LEN = 758       # ожидаемая длина свипа (3.2 → 14.3 ГГц)
+FREQ_SPAN_GHZ = 28.6  # полная двусторонняя полоса (-14.3 .. +14.3 ГГц)
+IFFT_LEN = ZEROS_LOW + ZEROS_MID + SWEEP_LEN  # = 1953

rfg_adc_plotter/gui/__init__.py

@@ -1,5 +0,0 @@
-"""GUI backends."""
-
-from rfg_adc_plotter.gui.pyqtgraph_backend import run_pyqtgraph
-
-__all__ = ["run_pyqtgraph"]

rfg_adc_plotter/gui/matplotlib_backend.py

@@ -0,0 +1,346 @@
+"""Matplotlib-бэкенд реалтайм-плоттера свипов."""
+
+import sys
+import threading
+from queue import Queue
+from typing import Optional, Tuple
+
+import numpy as np
+
+from rfg_adc_plotter.constants import FFT_LEN, FREQ_SPAN_GHZ, IFFT_LEN
+
+_IFFT_T_MAX_NS = float((IFFT_LEN - 1) / (FREQ_SPAN_GHZ * 1e9) * 1e9)
+from rfg_adc_plotter.io.sweep_reader import SweepReader
+from rfg_adc_plotter.processing.normalizer import build_calib_envelopes
+from rfg_adc_plotter.state.app_state import BACKGROUND_PATH, CALIB_ENVELOPE_PATH, AppState, format_status
+from rfg_adc_plotter.state.ring_buffer import RingBuffer
+from rfg_adc_plotter.types import SweepPacket
+
+
+def _parse_ylim(ylim_str: Optional[str]) -> Optional[Tuple[float, float]]:
+    if not ylim_str:
+        return None
+    try:
+        y0, y1 = ylim_str.split(",")
+        return (float(y0), float(y1))
+    except Exception:
+        sys.stderr.write("[warn] Некорректный формат --ylim, игнорирую. Ожидалось min,max\n")
+        return None
+
+
+def _parse_spec_clip(spec: Optional[str]) -> Optional[Tuple[float, float]]:
+    if not spec:
+        return None
+    s = str(spec).strip().lower()
+    if s in ("off", "none", "no"):
+        return None
+    try:
+        p0, p1 = s.replace(";", ",").split(",")
+        low, high = float(p0), float(p1)
+        if not (0.0 <= low < high <= 100.0):
+            return None
+        return (low, high)
+    except Exception:
+        return None
+
+
+def _visible_levels(data: np.ndarray, axis) -> Optional[Tuple[float, float]]:
+    """(vmin, vmax) по текущей видимой области imshow."""
+    if data.size == 0:
+        return None
+    ny, nx = data.shape[0], data.shape[1]
+    try:
+        x0, x1 = axis.get_xlim()
+        y0, y1 = axis.get_ylim()
+    except Exception:
+        x0, x1 = 0.0, float(nx - 1)
+        y0, y1 = 0.0, float(ny - 1)
+    xmin, xmax = sorted((float(x0), float(x1)))
+    ymin, ymax = sorted((float(y0), float(y1)))
+    ix0 = max(0, min(nx - 1, int(np.floor(xmin))))
+    ix1 = max(0, min(nx - 1, int(np.ceil(xmax))))
+    iy0 = max(0, min(ny - 1, int(np.floor(ymin))))
+    iy1 = max(0, min(ny - 1, int(np.ceil(ymax))))
+    if ix1 < ix0:
+        ix1 = ix0
+    if iy1 < iy0:
+        iy1 = iy0
+    sub = data[iy0 : iy1 + 1, ix0 : ix1 + 1]
+    finite = np.isfinite(sub)
+    if not finite.any():
+        return None
+    vals = sub[finite]
+    vmin = float(np.min(vals))
+    vmax = float(np.max(vals))
+    if not (np.isfinite(vmin) and np.isfinite(vmax)) or vmin == vmax:
+        return None
+    return (vmin, vmax)
+
+
+def run_matplotlib(args):
+    try:
+        import matplotlib
+        import matplotlib.pyplot as plt
+        from matplotlib.animation import FuncAnimation
+        from matplotlib.widgets import CheckButtons, Slider
+    except Exception as e:
+        sys.stderr.write(f"[error] Нужны matplotlib и её зависимости: {e}\n")
+        sys.exit(1)
+
+    q: Queue[SweepPacket] = Queue(maxsize=1000)
+    stop_event = threading.Event()
+    reader = SweepReader(args.port, args.baud, q, stop_event, fancy=bool(args.fancy))
+    reader.start()
+
+    max_sweeps = int(max(10, args.max_sweeps))
+    max_fps = max(1.0, float(args.max_fps))
+    interval_ms = int(1000.0 / max_fps)
+    spec_clip = _parse_spec_clip(getattr(args, "spec_clip", None))
+    spec_mean_sec = float(getattr(args, "spec_mean_sec", 0.0))
+    fixed_ylim = _parse_ylim(getattr(args, "ylim", None))
+    norm_type = str(getattr(args, "norm_type", "projector")).strip().lower()
+
+    state = AppState(norm_type=norm_type)
+    ring = RingBuffer(max_sweeps)
+
+    # --- Создание фигуры ---
+    fig, axs = plt.subplots(2, 2, figsize=(12, 8))
+    (ax_line, ax_img), (ax_fft, ax_spec) = axs
+    if hasattr(fig.canvas.manager, "set_window_title"):
+        fig.canvas.manager.set_window_title(args.title)
+    fig.subplots_adjust(wspace=0.25, hspace=0.35, left=0.07, right=0.90, top=0.92, bottom=0.08)
+
+    # Статусная строка
+    status_text = fig.text(0.01, 0.01, "", ha="left", va="bottom", fontsize=8, family="monospace")
+
+    # График последнего свипа
+    line_obj, = ax_line.plot([], [], lw=1, color="tab:blue")
+    line_norm_obj, = ax_line.plot([], [], lw=1, color="tab:green")
+    line_env_lo, = ax_line.plot([], [], lw=1, color="tab:orange", linestyle="--", alpha=0.7)
+    line_env_hi, = ax_line.plot([], [], lw=1, color="tab:orange", linestyle="--", alpha=0.7)
+    ax_line.set_title("Сырые данные", pad=1)
+    ax_line.set_xlabel("Частота, ГГц")
+    channel_text = ax_line.text(
+        0.98, 0.98, "", transform=ax_line.transAxes,
+        ha="right", va="top", fontsize=9, family="monospace",
+    )
+    if fixed_ylim is not None:
+        ax_line.set_ylim(fixed_ylim)
+
+    # График спектра
+    fft_line_obj, = ax_fft.plot([], [], lw=1)
+    ax_fft.set_title("FFT", pad=1)
+    ax_fft.set_xlabel("Время, нс")
+    ax_fft.set_ylabel("Мощность, дБ")
+
+    # Водопад сырых данных
+    img_obj = ax_img.imshow(
+        np.zeros((1, 1), dtype=np.float32),
+        aspect="auto", interpolation="nearest", origin="lower", cmap=args.cmap,
+    )
+    ax_img.set_title("Сырые данные", pad=12)
+    ax_img.set_ylabel("частота")
+    try:
+        ax_img.tick_params(axis="x", labelbottom=False)
+    except Exception:
+        pass
+
+    # Водопад спектров
+    img_fft_obj = ax_spec.imshow(
+        np.zeros((1, 1), dtype=np.float32),
+        aspect="auto", interpolation="nearest", origin="lower", cmap=args.cmap,
+    )
+    ax_spec.set_title("B-scan (дБ)", pad=12)
+    ax_spec.set_ylabel("Время, нс")
+    try:
+        ax_spec.tick_params(axis="x", labelbottom=False)
+    except Exception:
+        pass
+
+    # Слайдеры и чекбокс
+    contrast_slider = None
+    try:
+        fft_bins = ring.fft_bins
+        ax_smin = fig.add_axes([0.92, 0.55, 0.02, 0.35])
+        ax_smax = fig.add_axes([0.95, 0.55, 0.02, 0.35])
+        ax_sctr = fig.add_axes([0.98, 0.55, 0.02, 0.35])
+        ax_cb = fig.add_axes([0.92, 0.45, 0.08, 0.08])
+        ax_cb_file = fig.add_axes([0.92, 0.36, 0.08, 0.08])
+        ymin_slider = Slider(ax_smin, "Y min", 0, max(1, fft_bins - 1), valinit=0, valstep=1, orientation="vertical")
+        ymax_slider = Slider(ax_smax, "Y max", 0, max(1, fft_bins - 1), valinit=max(1, fft_bins - 1), valstep=1, orientation="vertical")
+        contrast_slider = Slider(ax_sctr, "Int max", 0, 100, valinit=100, valstep=1, orientation="vertical")
+        calib_cb = CheckButtons(ax_cb, ["калибровка"], [False])
+        calib_file_cb = CheckButtons(ax_cb_file, ["из файла"], [False])
+
+        import os as _os
+        if not _os.path.isfile(CALIB_ENVELOPE_PATH):
+            ax_cb_file.set_visible(False)
+
+        def _on_ylim_change(_val):
+            try:
+                y0 = int(min(ymin_slider.val, ymax_slider.val))
+                y1 = int(max(ymin_slider.val, ymax_slider.val))
+                ax_spec.set_ylim(y0, y1)
+                fig.canvas.draw_idle()
+            except Exception:
+                pass
+
+        def _on_calib_file_clicked(_v):
+            use_file = bool(calib_file_cb.get_status()[0])
+            if use_file:
+                ok = state.load_calib_envelope(CALIB_ENVELOPE_PATH)
+                if ok:
+                    state.set_calib_mode("file")
+                else:
+                    calib_file_cb.set_active(0)  # снять галочку
+            else:
+                state.set_calib_mode("live")
+            state.set_calib_enabled(bool(calib_cb.get_status()[0]))
+
+        def _on_calib_clicked(_v):
+            import os as _os2
+            if _os2.path.isfile(CALIB_ENVELOPE_PATH):
+                ax_cb_file.set_visible(True)
+            state.set_calib_enabled(bool(calib_cb.get_status()[0]))
+            fig.canvas.draw_idle()
+
+        ax_btn_bg = fig.add_axes([0.92, 0.27, 0.08, 0.05])
+        ax_cb_bg = fig.add_axes([0.92, 0.20, 0.08, 0.06])
+        from matplotlib.widgets import Button as MplButton
+        save_bg_btn = MplButton(ax_btn_bg, "Сохр. фон")
+        bg_cb = CheckButtons(ax_cb_bg, ["вычет фона"], [False])
+
+        def _on_save_bg(_event):
+            ok = state.save_background()
+            if ok:
+                state.load_background()
+            fig.canvas.draw_idle()
+
+        def _on_bg_clicked(_v):
+            state.set_background_enabled(bool(bg_cb.get_status()[0]))
+
+        save_bg_btn.on_clicked(_on_save_bg)
+        bg_cb.on_clicked(_on_bg_clicked)
+
+        ymin_slider.on_changed(_on_ylim_change)
+        ymax_slider.on_changed(_on_ylim_change)
+        contrast_slider.on_changed(lambda _v: fig.canvas.draw_idle())
+        calib_cb.on_clicked(_on_calib_clicked)
+        calib_file_cb.on_clicked(_on_calib_file_clicked)
+    except Exception:
+        calib_cb = None
+
+    FREQ_MIN = 3.323
+    FREQ_MAX = 14.323
+
+    # --- Инициализация imshow при первом свипе ---
+    def _init_imshow_extents():
+        w = ring.width
+        ms = ring.max_sweeps
+        fb = ring.fft_bins
+        img_obj.set_data(np.zeros((w, ms), dtype=np.float32))
+        img_obj.set_extent((0, ms - 1, FREQ_MIN, FREQ_MAX))
+        ax_img.set_xlim(0, ms - 1)
+        ax_img.set_ylim(FREQ_MIN, FREQ_MAX)
+        img_fft_obj.set_data(np.zeros((fb, ms), dtype=np.float32))
+        img_fft_obj.set_extent((0, ms - 1, 0.0, _IFFT_T_MAX_NS))
+        ax_spec.set_xlim(0, ms - 1)
+        ax_spec.set_ylim(0.0, _IFFT_T_MAX_NS)
+        ax_fft.set_xlim(0.0, _IFFT_T_MAX_NS)
+
+    _imshow_initialized = [False]
+
+    def update(_frame):
+        changed = state.drain_queue(q, ring) > 0
+
+        if changed and not _imshow_initialized[0] and ring.is_ready:
+            _init_imshow_extents()
+            _imshow_initialized[0] = True
+
+        # Линейный график свипа
+        if state.current_sweep_raw is not None:
+            raw = state.current_sweep_raw
+            if ring.x_shared is not None and raw.size <= ring.x_shared.size:
+                xs = ring.x_shared[: raw.size]
+            else:
+                xs = np.arange(raw.size, dtype=np.int32)
+            def _norm_to_max(data):
+                m = float(np.nanmax(np.abs(data)))
+                return data / m if m > 0.0 else data
+            line_obj.set_data(xs, _norm_to_max(raw))
+            if state.calib_mode == "file" and state.calib_file_envelope is not None:
+                upper = state.calib_file_envelope
+                lower = -upper
+                m_env = float(np.nanmax(np.abs(upper)))
+                if m_env <= 0.0:
+                    m_env = 1.0
+                line_env_lo.set_data(xs[: upper.size], lower / m_env)
+                line_env_hi.set_data(xs[: upper.size], upper / m_env)
+            elif state.last_calib_sweep is not None:
+                calib = state.last_calib_sweep
+                m_calib = float(np.nanmax(np.abs(calib)))
+                if m_calib <= 0.0:
+                    m_calib = 1.0
+                lower, upper = build_calib_envelopes(calib)
+                line_env_lo.set_data(xs[: calib.size], lower / m_calib)
+                line_env_hi.set_data(xs[: calib.size], upper / m_calib)
+            else:
+                line_env_lo.set_data([], [])
+                line_env_hi.set_data([], [])
+            if state.current_sweep_norm is not None:
+                line_norm_obj.set_data(xs[: state.current_sweep_norm.size], _norm_to_max(state.current_sweep_norm))
+            else:
+                line_norm_obj.set_data([], [])
+            ax_line.set_xlim(FREQ_MIN, FREQ_MAX)
+            if fixed_ylim is None:
+                ax_line.set_ylim(-1.05, 1.05)
+            ax_line.set_ylabel("/ max")
+
+            # Спектр — используем уже вычисленный в ring IFFT (временной профиль)
+            if ring.last_fft_vals is not None and ring.fft_time_axis is not None:
+                fft_vals = ring.last_fft_vals
+                xs_fft = ring.fft_time_axis
+                n = min(fft_vals.size, xs_fft.size)
+                fft_line_obj.set_data(xs_fft[:n], fft_vals[:n])
+                if np.isfinite(np.nanmin(fft_vals)) and np.isfinite(np.nanmax(fft_vals)):
+                    ax_fft.set_xlim(0, float(xs_fft[n - 1]))
+                    ax_fft.set_ylim(float(np.nanmin(fft_vals)), float(np.nanmax(fft_vals)))
+
+        # Водопад сырых данных
+        if changed and ring.is_ready:
+            disp = ring.get_display_ring()
+            if ring.x_shared is not None:
+                n = ring.x_shared.size
+                disp = disp[:n, :]
+            img_obj.set_data(disp)
+            img_obj.set_extent((0, ring.max_sweeps - 1, FREQ_MIN, FREQ_MAX))
+            ax_img.set_ylim(FREQ_MIN, FREQ_MAX)
+            levels = _visible_levels(disp, ax_img)
+            if levels is not None:
+                img_obj.set_clim(vmin=levels[0], vmax=levels[1])
+
+        # Водопад спектров
+        if changed and ring.is_ready:
+            disp_fft = ring.get_display_ring_fft()
+            disp_fft = ring.subtract_recent_mean_fft(disp_fft, spec_mean_sec)
+            img_fft_obj.set_data(disp_fft)
+            levels = ring.compute_fft_levels(disp_fft, spec_clip)
+            if levels is not None:
+                try:
+                    c = float(contrast_slider.val) / 100.0 if contrast_slider is not None else 1.0
+                except Exception:
+                    c = 1.0
+                vmax_eff = levels[0] + c * (levels[1] - levels[0])
+                img_fft_obj.set_clim(vmin=levels[0], vmax=vmax_eff)
+
+        # Статус и подпись канала
+        if changed and state.current_info:
+            status_text.set_text(format_status(state.current_info))
+            channel_text.set_text(state.format_channel_label())
+
+        return (line_obj, line_norm_obj, line_env_lo, line_env_hi, img_obj, fft_line_obj, img_fft_obj, status_text, channel_text)
+
+    ani = FuncAnimation(fig, update, interval=interval_ms, blit=False)
+    plt.show()
+    stop_event.set()
+    reader.join(timeout=1.0)

rfg_adc_plotter/io/__init__.py

@@ -1,6 +0,0 @@
-"""I/O helpers for serial sources and sweep parsing."""
-
-from rfg_adc_plotter.io.serial_source import SerialChunkReader, SerialLineSource
-from rfg_adc_plotter.io.sweep_reader import SweepReader
-
-__all__ = ["SerialChunkReader", "SerialLineSource", "SweepReader"]

rfg_adc_plotter/io/serial_source.py

@@ -1,6 +1,4 @@
-"""Serial input helpers with pyserial and raw TTY fallbacks."""
-
-from __future__ import annotations
+"""Источники последовательного ввода: обёртки над pyserial и raw TTY."""
 
 import io
 import os
@@ -14,13 +12,14 @@ def try_open_pyserial(path: str, baud: int, timeout: float):
     except Exception:
         return None
     try:
-        return serial.Serial(path, baudrate=baud, timeout=timeout)
+        ser = serial.Serial(path, baudrate=baud, timeout=timeout)
+        return ser
     except Exception:
         return None
 
 
 class FDReader:
-    """Buffered wrapper around a raw TTY file descriptor."""
+    """Простой враппер чтения строк из файлового дескриптора TTY."""
 
     def __init__(self, fd: int):
         self._fd = fd
@@ -34,7 +33,7 @@ class FDReader:
     def readline(self) -> bytes:
         return self._buf.readline()
 
-    def close(self) -> None:
+    def close(self):
         try:
             self._buf.close()
         except Exception:
@@ -42,7 +41,10 @@ class FDReader:
 
 
 def open_raw_tty(path: str, baud: int) -> Optional[FDReader]:
-    """Open a TTY without pyserial and configure it via termios."""
+    """Открыть TTY без pyserial и настроить порт через termios.
+
+    Возвращает FDReader или None при ошибке.
+    """
     try:
         import termios
         import tty
@@ -67,14 +69,17 @@ def open_raw_tty(path: str, baud: int) -> Optional[FDReader]:
             230400: getattr(termios, "B230400", None),
             460800: getattr(termios, "B460800", None),
         }
-        speed = baud_map.get(baud) or termios.B115200
+        b = baud_map.get(baud) or termios.B115200
 
-        attrs[4] = speed
-        attrs[5] = speed
+        attrs[4] = b  # ispeed
+        attrs[5] = b  # ospeed
+
+        # VMIN=1, VTIME=0 — блокирующее чтение по байту
         cc = attrs[6]
         cc[termios.VMIN] = 1
         cc[termios.VTIME] = 0
         attrs[6] = cc
+
         termios.tcsetattr(fd, termios.TCSANOW, attrs)
     except Exception:
         try:
@@ -87,11 +92,11 @@ def open_raw_tty(path: str, baud: int) -> Optional[FDReader]:
 
 
 class SerialLineSource:
-    """Unified line-oriented wrapper for pyserial and raw TTY readers."""
+    """Единый интерфейс для чтения строк из порта (pyserial или raw TTY)."""
 
     def __init__(self, path: str, baud: int, timeout: float = 1.0):
         self._pyserial = try_open_pyserial(path, baud, timeout)
-        self._fdreader: Optional[FDReader] = None
+        self._fdreader = None
         self._using = "pyserial" if self._pyserial is not None else "raw"
         if self._pyserial is None:
             self._fdreader = open_raw_tty(path, baud)
@@ -107,12 +112,13 @@ class SerialLineSource:
                 return self._pyserial.readline()
             except Exception:
                 return b""
-        try:
-            return self._fdreader.readline()  # type: ignore[union-attr]
-        except Exception:
-            return b""
+        else:
+            try:
+                return self._fdreader.readline()  # type: ignore[union-attr]
+            except Exception:
+                return b""
 
-    def close(self) -> None:
+    def close(self):
         try:
             if self._pyserial is not None:
                 self._pyserial.close()
@@ -123,7 +129,7 @@ class SerialLineSource:
 
 
 class SerialChunkReader:
-    """Fast non-blocking chunk reader for serial sources."""
+    """Быстрое неблокирующее чтение чанков из serial/raw TTY для максимального дренажа буфера."""
 
     def __init__(self, src: SerialLineSource):
         self._src = src
@@ -145,22 +151,20 @@ class SerialChunkReader:
                 self._fd = None
 
     def read_available(self) -> bytes:
-        """Return currently available bytes or b"" when nothing is ready."""
+        """Вернёт доступные байты (b"" если данных нет)."""
         if self._ser is not None:
             try:
-                available = int(getattr(self._ser, "in_waiting", 0))
+                n = int(getattr(self._ser, "in_waiting", 0))
             except Exception:
-                available = 0
-            if available > 0:
+                n = 0
+            if n > 0:
                 try:
-                    return self._ser.read(available)
+                    return self._ser.read(n)
                 except Exception:
                     return b""
             return b""
-
         if self._fd is None:
             return b""
-
         out = bytearray()
         while True:
             try:

rfg_adc_plotter/io/sweep_parser_core.py

@@ -1,427 +0,0 @@
-"""Reusable sweep parsers and sweep assembly helpers."""
-
-from __future__ import annotations
-
-import math
-import time
-from collections import deque
-from typing import List, Optional, Sequence, Set
-
-import numpy as np
-
-from rfg_adc_plotter.constants import DATA_INVERSION_THRESHOLD, LOG_BASE, LOG_EXP_LIMIT, LOG_POSTSCALER, LOG_SCALER
-from rfg_adc_plotter.types import ParserEvent, PointEvent, StartEvent, SweepAuxCurves, SweepInfo, SweepPacket
-
-
-def u32_to_i32(value: int) -> int:
-    return value - 0x1_0000_0000 if (value & 0x8000_0000) else value
-
-
-def u16_to_i16(value: int) -> int:
-    return value - 0x1_0000 if (value & 0x8000) else value
-
-
-def log_value_to_linear(value: int) -> float:
-    exponent = max(-LOG_EXP_LIMIT, min(LOG_EXP_LIMIT, float(value) * LOG_SCALER))
-    return float(LOG_BASE ** exponent)
-
-
-def log_pair_to_sweep(avg_1: int, avg_2: int) -> float:
-    value_1 = log_value_to_linear(avg_1)
-    value_2 = log_value_to_linear(avg_2)
-    return abs(value_1 - value_2) * LOG_POSTSCALER
-
-
-class AsciiSweepParser:
-    """Incremental parser for ASCII sweep streams."""
-
-    def __init__(self):
-        self._buf = bytearray()
-
-    def feed(self, data: bytes) -> List[ParserEvent]:
-        if data:
-            self._buf += data
-        events: List[ParserEvent] = []
-        while True:
-            nl = self._buf.find(b"\n")
-            if nl == -1:
-                break
-            line = bytes(self._buf[:nl])
-            del self._buf[: nl + 1]
-            if line.endswith(b"\r"):
-                line = line[:-1]
-            if not line:
-                continue
-            if line.startswith(b"Sweep_start"):
-                events.append(StartEvent())
-                continue
-
-            parts = line.split()
-            if len(parts) < 3:
-                continue
-            head = parts[0].lower()
-            try:
-                if head == b"s":
-                    if len(parts) >= 4:
-                        ch = int(parts[1], 10)
-                        x = int(parts[2], 10)
-                        y = int(parts[3], 10)
-                    else:
-                        ch = 0
-                        x = int(parts[1], 10)
-                        y = int(parts[2], 10)
-                elif head.startswith(b"s"):
-                    ch = int(head[1:], 10)
-                    x = int(parts[1], 10)
-                    y = int(parts[2], 10)
-                else:
-                    continue
-            except Exception:
-                continue
-            events.append(PointEvent(ch=int(ch), x=int(x), y=float(y)))
-        return events
-
-
-class LegacyBinaryParser:
-    """Byte-resynchronizing parser for legacy 8-byte binary records."""
-
-    def __init__(self):
-        self._buf = bytearray()
-
-    @staticmethod
-    def _u16_at(buf: bytearray, offset: int) -> int:
-        return int(buf[offset]) | (int(buf[offset + 1]) << 8)
-
-    def feed(self, data: bytes) -> List[ParserEvent]:
-        if data:
-            self._buf += data
-        events: List[ParserEvent] = []
-        while len(self._buf) >= 8:
-            w0 = self._u16_at(self._buf, 0)
-            w1 = self._u16_at(self._buf, 2)
-            w2 = self._u16_at(self._buf, 4)
-            if w0 == 0xFFFF and w1 == 0xFFFF and w2 == 0xFFFF and self._buf[6] == 0x0A:
-                events.append(StartEvent(ch=int(self._buf[7])))
-                del self._buf[:8]
-                continue
-            if self._buf[6] == 0x0A:
-                ch = int(self._buf[7])
-                value = u32_to_i32((w1 << 16) | w2)
-                events.append(PointEvent(ch=ch, x=int(w0), y=float(value)))
-                del self._buf[:8]
-                continue
-            del self._buf[:1]
-        return events
-
-
-class LogScaleBinaryParser32:
-    """Byte-resynchronizing parser for 32-bit logscale pair records."""
-
-    def __init__(self):
-        self._buf = bytearray()
-
-    @staticmethod
-    def _u16_at(buf: bytearray, offset: int) -> int:
-        return int(buf[offset]) | (int(buf[offset + 1]) << 8)
-
-    def feed(self, data: bytes) -> List[ParserEvent]:
-        if data:
-            self._buf += data
-        events: List[ParserEvent] = []
-        while len(self._buf) >= 12:
-            words = [self._u16_at(self._buf, idx * 2) for idx in range(6)]
-            if words[0:5] == [0xFFFF] * 5 and (words[5] & 0x00FF) == 0x000A:
-                events.append(StartEvent(ch=int((words[5] >> 8) & 0x00FF)))
-                del self._buf[:12]
-                continue
-            if (words[5] & 0x00FF) == 0x000A and words[0] != 0xFFFF:
-                ch = int((words[5] >> 8) & 0x00FF)
-                avg_1 = u32_to_i32((words[1] << 16) | words[2])
-                avg_2 = u32_to_i32((words[3] << 16) | words[4])
-                events.append(
-                    PointEvent(
-                        ch=ch,
-                        x=int(words[0]),
-                        y=log_pair_to_sweep(avg_1, avg_2),
-                        aux=(float(avg_1), float(avg_2)),
-                    )
-                )
-                del self._buf[:12]
-                continue
-            del self._buf[:1]
-        return events
-
-
-class LogScale16BitX2BinaryParser:
-    """Byte-resynchronizing parser for 16-bit x2 logscale records."""
-
-    def __init__(self):
-        self._buf = bytearray()
-        self._current_channel = 0
-
-    @staticmethod
-    def _u16_at(buf: bytearray, offset: int) -> int:
-        return int(buf[offset]) | (int(buf[offset + 1]) << 8)
-
-    def feed(self, data: bytes) -> List[ParserEvent]:
-        if data:
-            self._buf += data
-        events: List[ParserEvent] = []
-        while len(self._buf) >= 8:
-            words = [self._u16_at(self._buf, idx * 2) for idx in range(4)]
-            if words[0:3] == [0xFFFF, 0xFFFF, 0xFFFF] and (words[3] & 0x00FF) == 0x000A:
-                self._current_channel = int((words[3] >> 8) & 0x00FF)
-                events.append(StartEvent(ch=self._current_channel))
-                del self._buf[:8]
-                continue
-            if words[3] == 0xFFFF and words[0] != 0xFFFF:
-                avg_1 = u16_to_i16(words[1])
-                avg_2 = u16_to_i16(words[2])
-                events.append(
-                    PointEvent(
-                        ch=self._current_channel,
-                        x=int(words[0]),
-                        y=log_pair_to_sweep(avg_1, avg_2),
-                        aux=(float(avg_1), float(avg_2)),
-                    )
-                )
-                del self._buf[:8]
-                continue
-            del self._buf[:1]
-        return events
-
-
-class ParserTestStreamParser:
-    """Parser for the special test 16-bit x2 stream format."""
-
-    def __init__(self):
-        self._buf = bytearray()
-        self._buf_pos = 0
-        self._point_buf: list[int] = []
-        self._ffff_run = 0
-        self._current_channel = 0
-        self._expected_step: Optional[int] = None
-        self._in_sweep = False
-        self._local_resync = False
-
-    def _consume_point(self) -> Optional[PointEvent]:
-        if len(self._point_buf) != 3:
-            return None
-        step = int(self._point_buf[0])
-        if step <= 0:
-            return None
-        if self._expected_step is not None and step < self._expected_step:
-            return None
-        avg_1 = u16_to_i16(int(self._point_buf[1]))
-        avg_2 = u16_to_i16(int(self._point_buf[2]))
-        self._expected_step = step + 1
-        return PointEvent(
-            ch=self._current_channel,
-            x=step,
-            y=log_pair_to_sweep(avg_1, avg_2),
-            aux=(float(avg_1), float(avg_2)),
-        )
-
-    def feed(self, data: bytes) -> List[ParserEvent]:
-        if data:
-            self._buf += data
-        events: List[ParserEvent] = []
-
-        while (self._buf_pos + 1) < len(self._buf):
-            word = int(self._buf[self._buf_pos]) | (int(self._buf[self._buf_pos + 1]) << 8)
-            self._buf_pos += 2
-
-            if word == 0xFFFF:
-                self._ffff_run += 1
-                continue
-
-            if self._ffff_run > 0:
-                bad_point_on_delim = False
-                if self._in_sweep and self._point_buf and not self._local_resync:
-                    point = self._consume_point()
-                    if point is None:
-                        self._local_resync = True
-                        bad_point_on_delim = True
-                    else:
-                        events.append(point)
-                    self._point_buf.clear()
-
-                if self._ffff_run >= 2:
-                    if (word & 0x00FF) == 0x000A:
-                        self._current_channel = (word >> 8) & 0x00FF
-                        self._in_sweep = True
-                        self._expected_step = 1
-                        self._local_resync = False
-                        self._point_buf.clear()
-                        events.append(StartEvent(ch=self._current_channel))
-                        self._ffff_run = 0
-                        continue
-                    if self._in_sweep:
-                        self._local_resync = True
-                    self._ffff_run = 0
-                    continue
-
-                if self._local_resync and not bad_point_on_delim:
-                    self._local_resync = False
-                    self._point_buf.clear()
-                self._ffff_run = 0
-
-            if self._in_sweep and not self._local_resync:
-                self._point_buf.append(word)
-                if len(self._point_buf) > 3:
-                    self._point_buf.clear()
-                    self._local_resync = True
-
-        if self._buf_pos >= 262144:
-            del self._buf[: self._buf_pos]
-            self._buf_pos = 0
-        if (len(self._buf) - self._buf_pos) > 1_000_000:
-            tail = self._buf[self._buf_pos :]
-            if len(tail) > 262144:
-                tail = tail[-262144:]
-            self._buf = bytearray(tail)
-            self._buf_pos = 0
-        return events
-
-
-class SweepAssembler:
-    """Collect parser events into sweep packets matching runtime expectations."""
-
-    def __init__(self, fancy: bool = False, apply_inversion: bool = True):
-        self._fancy = bool(fancy)
-        self._apply_inversion = bool(apply_inversion)
-        self._max_width = 0
-        self._sweep_idx = 0
-        self._last_sweep_ts: Optional[float] = None
-        self._n_valid_hist = deque()
-        self._xs: list[int] = []
-        self._ys: list[float] = []
-        self._aux_1: list[float] = []
-        self._aux_2: list[float] = []
-        self._cur_channel: Optional[int] = None
-        self._cur_channels: set[int] = set()
-
-    def _reset_current(self) -> None:
-        self._xs.clear()
-        self._ys.clear()
-        self._aux_1.clear()
-        self._aux_2.clear()
-        self._cur_channel = None
-        self._cur_channels.clear()
-
-    def _scatter(self, xs: Sequence[int], values: Sequence[float], width: int) -> np.ndarray:
-        series = np.full((width,), np.nan, dtype=np.float32)
-        try:
-            idx = np.asarray(xs, dtype=np.int64)
-            vals = np.asarray(values, dtype=np.float32)
-            series[idx] = vals
-        except Exception:
-            for x, y in zip(xs, values):
-                xi = int(x)
-                if 0 <= xi < width:
-                    series[xi] = float(y)
-        return series
-
-    @staticmethod
-    def _fill_missing(series: np.ndarray) -> None:
-        known = ~np.isnan(series)
-        if not np.any(known):
-            return
-        known_idx = np.nonzero(known)[0]
-        for i0, i1 in zip(known_idx[:-1], known_idx[1:]):
-            if i1 - i0 > 1:
-                avg = (series[i0] + series[i1]) * 0.5
-                series[i0 + 1 : i1] = avg
-        first_idx = int(known_idx[0])
-        last_idx = int(known_idx[-1])
-        if first_idx > 0:
-            series[:first_idx] = series[first_idx]
-        if last_idx < series.size - 1:
-            series[last_idx + 1 :] = series[last_idx]
-
-    def consume(self, event: ParserEvent) -> Optional[SweepPacket]:
-        if isinstance(event, StartEvent):
-            packet = self.finalize_current()
-            self._reset_current()
-            if event.ch is not None:
-                self._cur_channel = int(event.ch)
-                self._cur_channels.add(int(event.ch))
-            return packet
-
-        if self._cur_channel is None:
-            self._cur_channel = int(event.ch)
-        self._cur_channels.add(int(event.ch))
-        self._xs.append(int(event.x))
-        self._ys.append(float(event.y))
-        if event.aux is not None:
-            self._aux_1.append(float(event.aux[0]))
-            self._aux_2.append(float(event.aux[1]))
-        return None
-
-    def finalize_current(self) -> Optional[SweepPacket]:
-        if not self._xs:
-            return None
-
-        ch_list = sorted(self._cur_channels) if self._cur_channels else [0]
-        ch_primary = ch_list[0] if ch_list else 0
-        width = max(int(max(self._xs)) + 1, 1)
-        self._max_width = max(self._max_width, width)
-        target_width = self._max_width if self._fancy else width
-
-        sweep = self._scatter(self._xs, self._ys, target_width)
-        aux_curves: SweepAuxCurves = None
-        if self._aux_1 and self._aux_2 and len(self._aux_1) == len(self._xs):
-            aux_curves = (
-                self._scatter(self._xs, self._aux_1, target_width),
-                self._scatter(self._xs, self._aux_2, target_width),
-            )
-
-        n_valid_cur = int(np.count_nonzero(np.isfinite(sweep)))
-
-        if self._fancy:
-            self._fill_missing(sweep)
-            if aux_curves is not None:
-                self._fill_missing(aux_curves[0])
-                self._fill_missing(aux_curves[1])
-
-        if self._apply_inversion:
-            try:
-                mean_value = float(np.nanmean(sweep))
-                if np.isfinite(mean_value) and mean_value < DATA_INVERSION_THRESHOLD:
-                    sweep *= -1.0
-            except Exception:
-                pass
-
-        self._sweep_idx += 1
-        now = time.time()
-        if self._last_sweep_ts is None:
-            dt_ms = float("nan")
-        else:
-            dt_ms = (now - self._last_sweep_ts) * 1000.0
-        self._last_sweep_ts = now
-
-        self._n_valid_hist.append((now, n_valid_cur))
-        while self._n_valid_hist and (now - self._n_valid_hist[0][0]) > 1.0:
-            self._n_valid_hist.popleft()
-        n_valid = float(sum(value for _ts, value in self._n_valid_hist) / len(self._n_valid_hist))
-
-        if n_valid_cur > 0:
-            vmin = float(np.nanmin(sweep))
-            vmax = float(np.nanmax(sweep))
-            mean = float(np.nanmean(sweep))
-            std = float(np.nanstd(sweep))
-        else:
-            vmin = vmax = mean = std = float("nan")
-
-        info: SweepInfo = {
-            "sweep": self._sweep_idx,
-            "ch": ch_primary,
-            "chs": ch_list,
-            "n_valid": n_valid,
-            "min": vmin,
-            "max": vmax,
-            "mean": mean,
-            "std": std,
-            "dt_ms": dt_ms,
-        }
-        return (sweep, info, aux_curves)

rfg_adc_plotter/io/sweep_reader.py

@@ -1,26 +1,21 @@
-"""Background sweep reader thread."""
-
-from __future__ import annotations
+"""Фоновый поток чтения и парсинга свипов из последовательного порта."""
 
 import sys
 import threading
 import time
+from collections import deque
 from queue import Full, Queue
+from typing import Optional
 
+import numpy as np
+
+from rfg_adc_plotter.constants import DATA_INVERSION_THRESHOLD
 from rfg_adc_plotter.io.serial_source import SerialChunkReader, SerialLineSource
-from rfg_adc_plotter.io.sweep_parser_core import (
-    AsciiSweepParser,
-    LegacyBinaryParser,
-    LogScale16BitX2BinaryParser,
-    LogScaleBinaryParser32,
-    ParserTestStreamParser,
-    SweepAssembler,
-)
-from rfg_adc_plotter.types import SweepPacket
+from rfg_adc_plotter.types import SweepInfo, SweepPacket
 
 
 class SweepReader(threading.Thread):
-    """Read a serial source in the background and emit completed sweep packets."""
+    """Фоновый поток: читает строки, формирует завершённые свипы и кладёт в очередь."""
 
     def __init__(
         self,
@@ -29,72 +24,192 @@ class SweepReader(threading.Thread):
         out_queue: "Queue[SweepPacket]",
         stop_event: threading.Event,
         fancy: bool = False,
-        bin_mode: bool = False,
-        logscale: bool = False,
-        parser_16_bit_x2: bool = False,
-        parser_test: bool = False,
     ):
         super().__init__(daemon=True)
         self._port_path = port_path
-        self._baud = int(baud)
-        self._queue = out_queue
+        self._baud = baud
+        self._q = out_queue
         self._stop = stop_event
+        self._src: Optional[SerialLineSource] = None
         self._fancy = bool(fancy)
-        self._bin_mode = bool(bin_mode)
-        self._logscale = bool(logscale)
-        self._parser_16_bit_x2 = bool(parser_16_bit_x2)
-        self._parser_test = bool(parser_test)
-        self._src: SerialLineSource | None = None
+        self._max_width: int = 0
+        self._sweep_idx: int = 0
+        self._last_sweep_ts: Optional[float] = None
+        self._n_valid_hist = deque()
 
-    def _build_parser(self):
-        if self._parser_test:
-            return ParserTestStreamParser(), SweepAssembler(fancy=self._fancy, apply_inversion=False)
-        if self._parser_16_bit_x2:
-            return LogScale16BitX2BinaryParser(), SweepAssembler(fancy=self._fancy, apply_inversion=False)
-        if self._logscale:
-            return LogScaleBinaryParser32(), SweepAssembler(fancy=self._fancy, apply_inversion=False)
-        if self._bin_mode:
-            return LegacyBinaryParser(), SweepAssembler(fancy=self._fancy, apply_inversion=True)
-        return AsciiSweepParser(), SweepAssembler(fancy=self._fancy, apply_inversion=True)
+    def _finalize_current(self, xs, ys, channels: Optional[set]):
+        if not xs:
+            return
+        ch_list = sorted(channels) if channels else [0]
+        ch_primary = ch_list[0] if ch_list else 0
+        max_x = max(xs)
+        width = max_x + 1
+        self._max_width = max(self._max_width, width)
+        target_width = self._max_width if self._fancy else width
 
-    def _enqueue(self, packet: SweepPacket) -> None:
+        sweep = np.full((target_width,), np.nan, dtype=np.float32)
         try:
-            self._queue.put_nowait(packet)
+            idx = np.asarray(xs, dtype=np.int64)
+            vals = np.asarray(ys, dtype=np.float32)
+            sweep[idx] = vals
+        except Exception:
+            for x, y in zip(xs, ys):
+                if 0 <= x < target_width:
+                    sweep[x] = float(y)
+
+        finite_pre = np.isfinite(sweep)
+        n_valid_cur = int(np.count_nonzero(finite_pre))
+
+        if self._fancy:
+            try:
+                known = ~np.isnan(sweep)
+                if np.any(known):
+                    known_idx = np.nonzero(known)[0]
+                    for i0, i1 in zip(known_idx[:-1], known_idx[1:]):
+                        if i1 - i0 > 1:
+                            avg = (sweep[i0] + sweep[i1]) * 0.5
+                            sweep[i0 + 1 : i1] = avg
+                    first_idx = int(known_idx[0])
+                    last_idx = int(known_idx[-1])
+                    if first_idx > 0:
+                        sweep[:first_idx] = sweep[first_idx]
+                    if last_idx < sweep.size - 1:
+                        sweep[last_idx + 1 :] = sweep[last_idx]
+            except Exception:
+                pass
+
+        try:
+            m = float(np.nanmean(sweep))
+            if np.isfinite(m) and m < DATA_INVERSION_THRESHOLD:
+                sweep *= -1.0
+        except Exception:
+            pass
+
+        self._sweep_idx += 1
+        if len(ch_list) > 1:
+            sys.stderr.write(
+                f"[warn] Sweep {self._sweep_idx}: изменялся номер канала: {ch_list}\n"
+            )
+        now = time.time()
+        if self._last_sweep_ts is None:
+            dt_ms = float("nan")
+        else:
+            dt_ms = (now - self._last_sweep_ts) * 1000.0
+        self._last_sweep_ts = now
+        self._n_valid_hist.append((now, n_valid_cur))
+        while self._n_valid_hist and (now - self._n_valid_hist[0][0]) > 1.0:
+            self._n_valid_hist.popleft()
+        if self._n_valid_hist:
+            n_valid = float(sum(v for _t, v in self._n_valid_hist) / len(self._n_valid_hist))
+        else:
+            n_valid = float(n_valid_cur)
+
+        if n_valid_cur > 0:
+            vmin = float(np.nanmin(sweep))
+            vmax = float(np.nanmax(sweep))
+            mean = float(np.nanmean(sweep))
+            std = float(np.nanstd(sweep))
+        else:
+            vmin = vmax = mean = std = float("nan")
+        info: SweepInfo = {
+            "sweep": self._sweep_idx,
+            "ch": ch_primary,
+            "chs": ch_list,
+            "n_valid": n_valid,
+            "min": vmin,
+            "max": vmax,
+            "mean": mean,
+            "std": std,
+            "dt_ms": dt_ms,
+        }
+
+        try:
+            self._q.put_nowait((sweep, info))
         except Full:
             try:
-                _ = self._queue.get_nowait()
+                _ = self._q.get_nowait()
             except Exception:
                 pass
             try:
-                self._queue.put_nowait(packet)
+                self._q.put_nowait((sweep, info))
             except Exception:
                 pass
 
-    def run(self) -> None:
+    def run(self):
+        xs: list = []
+        ys: list = []
+        cur_channel: Optional[int] = None
+        cur_channels: set = set()
+
         try:
             self._src = SerialLineSource(self._port_path, self._baud, timeout=1.0)
             sys.stderr.write(f"[info] Открыл порт {self._port_path} ({self._src._using})\n")
-        except Exception as exc:
-            sys.stderr.write(f"[error] {exc}\n")
+        except Exception as e:
+            sys.stderr.write(f"[error] {e}\n")
             return
 
-        parser, assembler = self._build_parser()
-
         try:
             chunk_reader = SerialChunkReader(self._src)
+            buf = bytearray()
             while not self._stop.is_set():
                 data = chunk_reader.read_available()
-                if not data:
+                if data:
+                    buf += data
+                else:
                     time.sleep(0.0005)
                     continue
-                for event in parser.feed(data):
-                    packet = assembler.consume(event)
-                    if packet is not None:
-                        self._enqueue(packet)
-            packet = assembler.finalize_current()
-            if packet is not None:
-                self._enqueue(packet)
+
+                while True:
+                    nl = buf.find(b"\n")
+                    if nl == -1:
+                        break
+                    line = bytes(buf[:nl])
+                    del buf[: nl + 1]
+                    if line.endswith(b"\r"):
+                        line = line[:-1]
+                    if not line:
+                        continue
+
+                    if line.startswith(b"Sweep_start"):
+                        self._finalize_current(xs, ys, cur_channels)
+                        xs.clear()
+                        ys.clear()
+                        cur_channel = None
+                        cur_channels.clear()
+                        continue
+
+                    if len(line) >= 3:
+                        parts = line.split()
+                        if len(parts) >= 3 and (parts[0].lower() == b"s" or parts[0].lower().startswith(b"s")):
+                            try:
+                                if parts[0].lower() == b"s":
+                                    if len(parts) >= 4:
+                                        ch = int(parts[1], 10)
+                                        x = int(parts[2], 10)
+                                        y = int(parts[3], 10)
+                                    else:
+                                        ch = 0
+                                        x = int(parts[1], 10)
+                                        y = int(parts[2], 10)
+                                else:
+                                    ch = int(parts[0][1:], 10)
+                                    x = int(parts[1], 10)
+                                    y = int(parts[2], 10)
+                            except Exception:
+                                continue
+                            if cur_channel is None:
+                                cur_channel = ch
+                            cur_channels.add(ch)
+                            xs.append(x)
+                            ys.append(y)
+
+                if len(buf) > 1_000_000:
+                    del buf[:-262144]
         finally:
+            try:
+                self._finalize_current(xs, ys, cur_channels)
+            except Exception:
+                pass
             try:
                 if self._src is not None:
                     self._src.close()

rfg_adc_plotter/main.py

@@ -1,25 +1,107 @@
-"""Main entrypoint for the modularized ADC plotter."""
+#!/usr/bin/env python3
+"""
+Реалтайм-плоттер для свипов из виртуального COM-порта.
 
-from __future__ import annotations
+Формат строк:
+  - "Sweep_start" — начало нового свипа (предыдущий считается завершённым)
+  - "s CH X Y" — точка (номер канала, индекс X, значение Y), все целые со знаком
 
+Отрисовываются четыре графика:
+  - Сырые данные: последний полученный свип (Y vs X)
+  - Водопад сырых данных: последние N свипов
+  - FFT текущего свипа
+  - B-scan: водопад FFT-строк
+
+Зависимости: numpy. PySerial опционален — при его отсутствии
+используется сырой доступ к TTY через termios.
+GUI: matplotlib (совместимый) или pyqtgraph (быстрый).
+"""
+
+import argparse
 import sys
 
-from rfg_adc_plotter.cli import build_parser
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Читает свипы из виртуального COM-порта и рисует: "
+            "последний свип и водопад (реалтайм)."
+        )
+    )
+    parser.add_argument(
+        "port",
+        help="Путь к порту, например /dev/ttyACM1 или COM3 (COM10+: \\\\.\\COM10)",
+    )
+    parser.add_argument("--baud", type=int, default=115200, help="Скорость (по умолчанию 115200)")
+    parser.add_argument("--max-sweeps", type=int, default=200, help="Количество видимых свипов в водопаде")
+    parser.add_argument("--max-fps", type=float, default=30.0, help="Лимит частоты отрисовки, кадров/с")
+    parser.add_argument("--cmap", default="viridis", help="Цветовая карта водопада")
+    parser.add_argument(
+        "--spec-clip",
+        default="2,98",
+        help=(
+            "Процентильная обрезка уровней водопада спектров, %% (min,max). "
+            "Напр. 2,98. 'off' — отключить"
+        ),
+    )
+    parser.add_argument(
+        "--spec-mean-sec",
+        type=float,
+        default=0.0,
+        help=(
+            "Вычитание среднего по каждой частоте за последние N секунд "
+            "в водопаде спектров (0 — отключить)"
+        ),
+    )
+    parser.add_argument("--title", default="ADC Sweeps", help="Заголовок окна")
+    parser.add_argument(
+        "--fancy",
+        action="store_true",
+        help="Заполнять выпавшие точки средними значениями между соседними",
+    )
+    parser.add_argument(
+        "--ylim",
+        type=str,
+        default=None,
+        help="Фиксированные Y-пределы для кривой формата min,max (например -1000,1000). По умолчанию авто",
+    )
+    parser.add_argument(
+        "--backend",
+        choices=["auto", "pg", "mpl"],
+        default="auto",
+        help="Графический бэкенд: pyqtgraph (pg) — быстрее; matplotlib (mpl) — совместимый. По умолчанию auto",
+    )
+    parser.add_argument(
+        "--norm-type",
+        choices=["projector", "simple"],
+        default="projector",
+        help="Тип нормировки: projector (по огибающим в [-1000,+1000]) или simple (raw/calib)",
+    )
+    return parser
 
 
-def main() -> None:
+def main():
     args = build_parser().parse_args()
-    if args.backend == "mpl":
-        sys.stderr.write("[error] Matplotlib backend removed. Use --backend pg or --backend auto.\n")
-        raise SystemExit(2)
 
-    from rfg_adc_plotter.gui.pyqtgraph_backend import run_pyqtgraph
+    if args.backend == "pg":
+        from rfg_adc_plotter.gui.pyqtgraph_backend import run_pyqtgraph
+        try:
+            run_pyqtgraph(args)
+        except Exception as e:
+            sys.stderr.write(f"[error] PyQtGraph бэкенд недоступен: {e}\n")
+            sys.exit(1)
+        return
 
-    try:
-        run_pyqtgraph(args)
-    except Exception as exc:
-        sys.stderr.write(f"[error] PyQtGraph бэкенд недоступен: {exc}\n")
-        raise SystemExit(1) from exc
+    if args.backend == "auto":
+        try:
+            from rfg_adc_plotter.gui.pyqtgraph_backend import run_pyqtgraph
+            run_pyqtgraph(args)
+            return
+        except Exception:
+            pass  # Откатываемся на matplotlib
+
+    from rfg_adc_plotter.gui.matplotlib_backend import run_matplotlib
+    run_matplotlib(args)
 
 
 if __name__ == "__main__":

rfg_adc_plotter/processing/__init__.py

@@ -1,67 +0,0 @@
-"""Pure sweep-processing helpers."""
-
-from rfg_adc_plotter.processing.background import (
-    load_fft_background,
-    save_fft_background,
-    subtract_fft_background,
-    validate_fft_background,
-)
-from rfg_adc_plotter.processing.calibration import (
-    build_calib_envelope,
-    calibrate_freqs,
-    get_calibration_base,
-    get_calibration_coeffs,
-    load_calib_envelope,
-    recalculate_calibration_c,
-    save_calib_envelope,
-    set_calibration_base_value,
-)
-from rfg_adc_plotter.processing.fft import (
-    compute_distance_axis,
-    compute_fft_mag_row,
-    compute_fft_row,
-    fft_mag_to_db,
-)
-from rfg_adc_plotter.processing.formatting import (
-    compute_auto_ylim,
-    format_status_kv,
-    parse_spec_clip,
-)
-from rfg_adc_plotter.processing.normalization import (
-    build_calib_envelopes,
-    normalize_by_envelope,
-    normalize_by_calib,
-)
-from rfg_adc_plotter.processing.peaks import (
-    find_peak_width_markers,
-    find_top_peaks_over_ref,
-    rolling_median_ref,
-)
-
-__all__ = [
-    "build_calib_envelopes",
-    "build_calib_envelope",
-    "calibrate_freqs",
-    "compute_auto_ylim",
-    "compute_distance_axis",
-    "compute_fft_mag_row",
-    "compute_fft_row",
-    "fft_mag_to_db",
-    "find_peak_width_markers",
-    "find_top_peaks_over_ref",
-    "format_status_kv",
-    "get_calibration_base",
-    "get_calibration_coeffs",
-    "load_calib_envelope",
-    "load_fft_background",
-    "normalize_by_envelope",
-    "normalize_by_calib",
-    "parse_spec_clip",
-    "recalculate_calibration_c",
-    "rolling_median_ref",
-    "save_calib_envelope",
-    "save_fft_background",
-    "set_calibration_base_value",
-    "subtract_fft_background",
-    "validate_fft_background",
-]

rfg_adc_plotter/processing/background.py

@@ -1,66 +0,0 @@
-"""Helpers for persisted FFT background profiles."""
-
-from __future__ import annotations
-
-from pathlib import Path
-
-import numpy as np
-
-
-def validate_fft_background(background: np.ndarray) -> np.ndarray:
-    """Validate a saved FFT background payload."""
-    values = np.asarray(background)
-    if values.ndim != 1:
-        raise ValueError("FFT background must be a 1D array")
-    if not np.issubdtype(values.dtype, np.number):
-        raise ValueError("FFT background must be numeric")
-    values = np.asarray(values, dtype=np.float32).reshape(-1)
-    if values.size == 0:
-        raise ValueError("FFT background is empty")
-    return values
-
-
-def _normalize_background_path(path: str | Path) -> Path:
-    out = Path(path).expanduser()
-    if out.suffix.lower() != ".npy":
-        out = out.with_suffix(".npy")
-    return out
-
-
-def save_fft_background(path: str | Path, background: np.ndarray) -> str:
-    """Persist an FFT background profile as a .npy file."""
-    normalized_path = _normalize_background_path(path)
-    values = validate_fft_background(background)
-    np.save(normalized_path, values.astype(np.float32, copy=False))
-    return str(normalized_path)
-
-
-def load_fft_background(path: str | Path) -> np.ndarray:
-    """Load and validate an FFT background profile from a .npy file."""
-    normalized_path = _normalize_background_path(path)
-    loaded = np.load(normalized_path, allow_pickle=False)
-    return validate_fft_background(loaded)
-
-
-def subtract_fft_background(signal_mag: np.ndarray, background_mag: np.ndarray) -> np.ndarray:
-    """Subtract a background profile from FFT magnitudes in linear amplitude."""
-    signal = np.asarray(signal_mag, dtype=np.float32)
-    background = validate_fft_background(background_mag)
-    if signal.ndim == 1:
-        if signal.size != background.size:
-            raise ValueError("FFT background size does not match signal size")
-        valid = np.isfinite(signal) & np.isfinite(background)
-        out = np.full_like(signal, np.nan, dtype=np.float32)
-        if np.any(valid):
-            out[valid] = np.maximum(signal[valid] - background[valid], 0.0)
-        return out
-
-    if signal.ndim == 2:
-        if signal.shape[0] != background.size:
-            raise ValueError("FFT background size does not match signal rows")
-        background_2d = background[:, None]
-        valid = np.isfinite(signal) & np.isfinite(background_2d)
-        diff = signal - background_2d
-        return np.where(valid, np.maximum(diff, 0.0), np.nan).astype(np.float32, copy=False)
-
-    raise ValueError("FFT background subtraction supports only 1D or 2D signals")

rfg_adc_plotter/processing/calibration.py

@@ -1,124 +0,0 @@
-"""Frequency-axis calibration helpers."""
-
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any, Mapping
-
-import numpy as np
-
-from rfg_adc_plotter.constants import SWEEP_FREQ_MAX_GHZ, SWEEP_FREQ_MIN_GHZ
-from rfg_adc_plotter.processing.normalization import build_calib_envelopes
-from rfg_adc_plotter.types import SweepData
-
-
-def recalculate_calibration_c(
-    base_coeffs: np.ndarray,
-    f_min: float = SWEEP_FREQ_MIN_GHZ,
-    f_max: float = SWEEP_FREQ_MAX_GHZ,
-) -> np.ndarray:
-    """Recalculate coefficients while preserving sweep edges."""
-    coeffs = np.asarray(base_coeffs, dtype=np.float64).reshape(-1)
-    if coeffs.size < 3:
-        out = np.zeros((3,), dtype=np.float64)
-        out[: coeffs.size] = coeffs
-        coeffs = out
-    c0, c1, c2 = float(coeffs[0]), float(coeffs[1]), float(coeffs[2])
-    x0 = float(f_min)
-    x1 = float(f_max)
-    y0 = c0 + c1 * x0 + c2 * (x0 ** 2)
-    y1 = c0 + c1 * x1 + c2 * (x1 ** 2)
-    if not (np.isfinite(y0) and np.isfinite(y1)) or y1 == y0:
-        return np.asarray([c0, c1, c2], dtype=np.float64)
-    scale = (x1 - x0) / (y1 - y0)
-    shift = x0 - scale * y0
-    return np.asarray(
-        [
-            shift + scale * c0,
-            scale * c1,
-            scale * c2,
-        ],
-        dtype=np.float64,
-    )
-
-
-CALIBRATION_C_BASE = np.asarray([0.0, 1.0, 0.025], dtype=np.float64)
-CALIBRATION_C = recalculate_calibration_c(CALIBRATION_C_BASE)
-
-
-def get_calibration_base() -> np.ndarray:
-    return np.asarray(CALIBRATION_C_BASE, dtype=np.float64).copy()
-
-
-def get_calibration_coeffs() -> np.ndarray:
-    return np.asarray(CALIBRATION_C, dtype=np.float64).copy()
-
-
-def set_calibration_base_value(index: int, value: float) -> np.ndarray:
-    """Update one base coefficient and recalculate the working coefficients."""
-    global CALIBRATION_C
-    CALIBRATION_C_BASE[int(index)] = float(value)
-    CALIBRATION_C = recalculate_calibration_c(CALIBRATION_C_BASE)
-    return get_calibration_coeffs()
-
-
-def calibrate_freqs(sweep: Mapping[str, Any]) -> SweepData:
-    """Return a sweep copy with calibrated and resampled frequency axis."""
-    freqs = np.asarray(sweep["F"], dtype=np.float64).copy()
-    values = np.asarray(sweep["I"], dtype=np.float64).copy()
-    coeffs = np.asarray(CALIBRATION_C, dtype=np.float64)
-    if freqs.size > 0:
-        freqs = coeffs[0] + coeffs[1] * freqs + coeffs[2] * (freqs * freqs)
-
-    if freqs.size >= 2:
-        freqs_cal = np.linspace(float(freqs[0]), float(freqs[-1]), freqs.size, dtype=np.float64)
-        values_cal = np.interp(freqs_cal, freqs, values).astype(np.float64)
-    else:
-        freqs_cal = freqs.copy()
-        values_cal = values.copy()
-
-    return {
-        "F": freqs_cal,
-        "I": values_cal,
-    }
-
-
-def build_calib_envelope(sweep: np.ndarray) -> np.ndarray:
-    """Build the active calibration envelope from a raw sweep."""
-    values = np.asarray(sweep, dtype=np.float32).reshape(-1)
-    if values.size == 0:
-        raise ValueError("Calibration sweep is empty")
-    _, upper = build_calib_envelopes(values)
-    return np.asarray(upper, dtype=np.float32)
-
-
-def validate_calib_envelope(envelope: np.ndarray) -> np.ndarray:
-    """Validate a saved calibration envelope payload."""
-    values = np.asarray(envelope, dtype=np.float32).reshape(-1)
-    if values.size == 0:
-        raise ValueError("Calibration envelope is empty")
-    if not np.issubdtype(values.dtype, np.number):
-        raise ValueError("Calibration envelope must be numeric")
-    return values
-
-
-def _normalize_calib_path(path: str | Path) -> Path:
-    out = Path(path).expanduser()
-    if out.suffix.lower() != ".npy":
-        out = out.with_suffix(".npy")
-    return out
-
-
-def save_calib_envelope(path: str | Path, envelope: np.ndarray) -> str:
-    """Persist a calibration envelope as a .npy file and return the final path."""
-    normalized_path = _normalize_calib_path(path)
-    values = validate_calib_envelope(envelope)
-    np.save(normalized_path, values.astype(np.float32, copy=False))
-    return str(normalized_path)
-
-
-def load_calib_envelope(path: str | Path) -> np.ndarray:
-    """Load and validate a calibration envelope from a .npy file."""
-    normalized_path = _normalize_calib_path(path)
-    loaded = np.load(normalized_path, allow_pickle=False)
-    return validate_calib_envelope(loaded)

rfg_adc_plotter/processing/fft.py

@@ -1,267 +0,0 @@
-"""FFT helpers for line and waterfall views."""
-
-from __future__ import annotations
-
-from typing import Optional, Tuple
-
-import numpy as np
-
-from rfg_adc_plotter.constants import C_M_S, FFT_LEN, SWEEP_FREQ_MAX_GHZ, SWEEP_FREQ_MIN_GHZ
-
-
-def _finite_freq_bounds(freqs: Optional[np.ndarray]) -> Optional[Tuple[float, float]]:
-    """Return finite frequency bounds for the current working segment."""
-    if freqs is None:
-        return None
-    freq_arr = np.asarray(freqs, dtype=np.float64).reshape(-1)
-    finite = freq_arr[np.isfinite(freq_arr)]
-    if finite.size < 2:
-        return None
-    f_min = float(np.min(finite))
-    f_max = float(np.max(finite))
-    if not np.isfinite(f_min) or not np.isfinite(f_max) or f_max <= f_min:
-        return None
-    return f_min, f_max
-
-
-def prepare_fft_segment(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-    fft_len: int = FFT_LEN,
-) -> Optional[Tuple[np.ndarray, int]]:
-    """Prepare a sweep segment for FFT on a uniform frequency grid."""
-    take_fft = min(int(sweep.size), int(fft_len))
-    if take_fft <= 0:
-        return None
-
-    sweep_seg = np.asarray(sweep[:take_fft], dtype=np.float32)
-    fallback = np.nan_to_num(sweep_seg, nan=0.0).astype(np.float32, copy=False)
-    if freqs is None:
-        return fallback, take_fft
-
-    freq_arr = np.asarray(freqs)
-    if freq_arr.size < take_fft:
-        return fallback, take_fft
-
-    freq_seg = np.asarray(freq_arr[:take_fft], dtype=np.float64)
-    valid = np.isfinite(sweep_seg) & np.isfinite(freq_seg)
-    if int(np.count_nonzero(valid)) < 2:
-        return fallback, take_fft
-
-    x_valid = freq_seg[valid]
-    y_valid = sweep_seg[valid]
-    order = np.argsort(x_valid, kind="mergesort")
-    x_valid = x_valid[order]
-    y_valid = y_valid[order]
-    x_unique, unique_idx = np.unique(x_valid, return_index=True)
-    y_unique = y_valid[unique_idx]
-    if x_unique.size < 2 or x_unique[-1] <= x_unique[0]:
-        return fallback, take_fft
-
-    x_uniform = np.linspace(float(x_unique[0]), float(x_unique[-1]), take_fft, dtype=np.float64)
-    resampled = np.interp(x_uniform, x_unique, y_unique).astype(np.float32)
-    return resampled, take_fft
-
-
-def build_symmetric_ifft_spectrum(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-    fft_len: int = FFT_LEN,
-) -> Optional[np.ndarray]:
-    """Build a centered symmetric spectrum over [-f_max, f_max] for IFFT."""
-    if fft_len <= 0:
-        return None
-
-    bounds = _finite_freq_bounds(freqs)
-    if bounds is None:
-        f_min = float(SWEEP_FREQ_MIN_GHZ)
-        f_max = float(SWEEP_FREQ_MAX_GHZ)
-    else:
-        f_min, f_max = bounds
-
-    freq_axis = np.linspace(-f_max, f_max, int(fft_len), dtype=np.float64)
-    neg_idx_all = np.flatnonzero(freq_axis <= (-f_min))
-    pos_idx_all = np.flatnonzero(freq_axis >= f_min)
-    band_len = int(min(neg_idx_all.size, pos_idx_all.size))
-    if band_len <= 1:
-        return None
-
-    neg_idx = neg_idx_all[:band_len]
-    pos_idx = pos_idx_all[-band_len:]
-    prepared = prepare_fft_segment(sweep, freqs, fft_len=band_len)
-    if prepared is None:
-        return None
-
-    fft_seg, take_fft = prepared
-    if take_fft != band_len:
-        fft_seg = np.asarray(fft_seg[:band_len], dtype=np.float32)
-        if fft_seg.size < band_len:
-            padded = np.zeros((band_len,), dtype=np.float32)
-            padded[: fft_seg.size] = fft_seg
-            fft_seg = padded
-
-    window = np.hanning(band_len).astype(np.float32)
-    band = np.nan_to_num(fft_seg, nan=0.0).astype(np.float32, copy=False) * window
-
-    spectrum = np.zeros((int(fft_len),), dtype=np.float32)
-    spectrum[pos_idx] = band
-    spectrum[neg_idx] = band[::-1]
-    return spectrum
-
-
-def build_positive_only_centered_ifft_spectrum(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-    fft_len: int = FFT_LEN,
-) -> Optional[np.ndarray]:
-    """Build a centered spectrum with zeros from -f_max to +f_min."""
-    if fft_len <= 0:
-        return None
-
-    bounds = _finite_freq_bounds(freqs)
-    if bounds is None:
-        f_min = float(SWEEP_FREQ_MIN_GHZ)
-        f_max = float(SWEEP_FREQ_MAX_GHZ)
-    else:
-        f_min, f_max = bounds
-
-    freq_axis = np.linspace(-f_max, f_max, int(fft_len), dtype=np.float64)
-    pos_idx = np.flatnonzero(freq_axis >= f_min)
-    band_len = int(pos_idx.size)
-    if band_len <= 1:
-        return None
-
-    prepared = prepare_fft_segment(sweep, freqs, fft_len=band_len)
-    if prepared is None:
-        return None
-
-    fft_seg, take_fft = prepared
-    if take_fft != band_len:
-        fft_seg = np.asarray(fft_seg[:band_len], dtype=np.float32)
-        if fft_seg.size < band_len:
-            padded = np.zeros((band_len,), dtype=np.float32)
-            padded[: fft_seg.size] = fft_seg
-            fft_seg = padded
-
-    window = np.hanning(band_len).astype(np.float32)
-    band = np.nan_to_num(fft_seg, nan=0.0).astype(np.float32, copy=False) * window
-
-    spectrum = np.zeros((int(fft_len),), dtype=np.float32)
-    spectrum[pos_idx] = band
-    return spectrum
-
-
-def fft_mag_to_db(mag: np.ndarray) -> np.ndarray:
-    """Convert magnitude to dB with safe zero handling."""
-    mag_arr = np.asarray(mag, dtype=np.float32)
-    safe_mag = np.maximum(mag_arr, 0.0)
-    return (20.0 * np.log10(safe_mag + 1e-9)).astype(np.float32, copy=False)
-
-
-def _compute_fft_mag_row_direct(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-    bins: int,
-) -> np.ndarray:
-    prepared = prepare_fft_segment(sweep, freqs, fft_len=FFT_LEN)
-    if prepared is None:
-        return np.full((bins,), np.nan, dtype=np.float32)
-
-    fft_seg, take_fft = prepared
-    fft_in = np.zeros((FFT_LEN,), dtype=np.float32)
-    window = np.hanning(take_fft).astype(np.float32)
-    fft_in[:take_fft] = fft_seg * window
-    spec = np.fft.ifft(fft_in)
-    mag = np.abs(spec).astype(np.float32)
-    if mag.shape[0] != bins:
-        mag = mag[:bins]
-    return mag
-
-
-def _normalize_fft_mode(mode: str | None, symmetric: Optional[bool]) -> str:
-    if symmetric is not None:
-        return "symmetric" if symmetric else "direct"
-    normalized = str(mode or "symmetric").strip().lower()
-    if normalized in {"direct", "ordinary", "normal"}:
-        return "direct"
-    if normalized in {"symmetric", "sym", "mirror"}:
-        return "symmetric"
-    if normalized in {"positive_only", "positive-centered", "positive_centered", "zero_left"}:
-        return "positive_only"
-    raise ValueError(f"Unsupported FFT mode: {mode!r}")
-
-
-def compute_fft_mag_row(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-    bins: int,
-    *,
-    mode: str = "symmetric",
-    symmetric: Optional[bool] = None,
-) -> np.ndarray:
-    """Compute a linear FFT magnitude row."""
-    if bins <= 0:
-        return np.zeros((0,), dtype=np.float32)
-
-    fft_mode = _normalize_fft_mode(mode, symmetric)
-    if fft_mode == "direct":
-        return _compute_fft_mag_row_direct(sweep, freqs, bins)
-
-    if fft_mode == "positive_only":
-        spectrum_centered = build_positive_only_centered_ifft_spectrum(sweep, freqs, fft_len=FFT_LEN)
-    else:
-        spectrum_centered = build_symmetric_ifft_spectrum(sweep, freqs, fft_len=FFT_LEN)
-    if spectrum_centered is None:
-        return np.full((bins,), np.nan, dtype=np.float32)
-
-    spec = np.fft.ifft(np.fft.ifftshift(spectrum_centered))
-    mag = np.abs(spec).astype(np.float32)
-    if mag.shape[0] != bins:
-        mag = mag[:bins]
-    return mag
-
-
-def compute_fft_row(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-    bins: int,
-    *,
-    mode: str = "symmetric",
-    symmetric: Optional[bool] = None,
-) -> np.ndarray:
-    """Compute a dB FFT row."""
-    return fft_mag_to_db(compute_fft_mag_row(sweep, freqs, bins, mode=mode, symmetric=symmetric))
-
-
-def compute_distance_axis(
-    freqs: Optional[np.ndarray],
-    bins: int,
-    *,
-    mode: str = "symmetric",
-    symmetric: Optional[bool] = None,
-) -> np.ndarray:
-    """Compute the one-way distance axis for IFFT output."""
-    if bins <= 0:
-        return np.zeros((0,), dtype=np.float64)
-    fft_mode = _normalize_fft_mode(mode, symmetric)
-    if fft_mode in {"symmetric", "positive_only"}:
-        bounds = _finite_freq_bounds(freqs)
-        if bounds is None:
-            f_max = float(SWEEP_FREQ_MAX_GHZ)
-        else:
-            _, f_max = bounds
-        df_ghz = (2.0 * f_max) / max(1, FFT_LEN - 1)
-    else:
-        if freqs is None:
-            return np.arange(bins, dtype=np.float64)
-        freq_arr = np.asarray(freqs, dtype=np.float64)
-        finite = freq_arr[np.isfinite(freq_arr)]
-        if finite.size < 2:
-            return np.arange(bins, dtype=np.float64)
-        df_ghz = float((finite[-1] - finite[0]) / max(1, finite.size - 1))
-    df_hz = abs(df_ghz) * 1e9
-    if not np.isfinite(df_hz) or df_hz <= 0.0:
-        return np.arange(bins, dtype=np.float64)
-
-    step_m = C_M_S / (2.0 * FFT_LEN * df_hz)
-    return np.arange(bins, dtype=np.float64) * step_m

rfg_adc_plotter/processing/formatting.py

@@ -1,71 +0,0 @@
-"""Formatting and display-range helpers."""
-
-from __future__ import annotations
-
-from typing import Any, Mapping, Optional, Tuple
-
-import numpy as np
-
-
-def format_status_kv(data: Mapping[str, Any]) -> str:
-    """Convert status metrics into a compact single-line representation."""
-
-    def _fmt(value: Any) -> str:
-        if value is None:
-            return "NA"
-        try:
-            f_value = float(value)
-        except Exception:
-            return str(value)
-        if not np.isfinite(f_value):
-            return "nan"
-        if abs(f_value) >= 1000 or (0 < abs(f_value) < 0.01):
-            return f"{f_value:.3g}"
-        return f"{f_value:.3f}".rstrip("0").rstrip(".")
-
-    return "  ".join(f"{key}:{_fmt(value)}" for key, value in data.items())
-
-
-def parse_spec_clip(spec: Optional[str]) -> Optional[Tuple[float, float]]:
-    """Parse a waterfall percentile clip specification."""
-    if not spec:
-        return None
-    value = str(spec).strip().lower()
-    if value in ("off", "none", "no"):
-        return None
-    try:
-        p0, p1 = value.replace(";", ",").split(",")
-        low = float(p0)
-        high = float(p1)
-        if not (0.0 <= low < high <= 100.0):
-            return None
-        return (low, high)
-    except Exception:
-        return None
-
-
-def compute_auto_ylim(*series_list: Optional[np.ndarray]) -> Optional[Tuple[float, float]]:
-    """Compute a common Y-range with a small padding."""
-    y_min: Optional[float] = None
-    y_max: Optional[float] = None
-    for series in series_list:
-        if series is None:
-            continue
-        arr = np.asarray(series)
-        if arr.size == 0:
-            continue
-        finite = arr[np.isfinite(arr)]
-        if finite.size == 0:
-            continue
-        cur_min = float(np.min(finite))
-        cur_max = float(np.max(finite))
-        y_min = cur_min if y_min is None else min(y_min, cur_min)
-        y_max = cur_max if y_max is None else max(y_max, cur_max)
-
-    if y_min is None or y_max is None:
-        return None
-    if y_min == y_max:
-        pad = max(1.0, abs(y_min) * 0.05)
-    else:
-        pad = 0.05 * (y_max - y_min)
-    return (y_min - pad, y_max + pad)

rfg_adc_plotter/processing/normalization.py

@@ -1,173 +0,0 @@
-"""Sweep normalization helpers."""
-
-from __future__ import annotations
-
-from typing import Tuple
-
-import numpy as np
-
-
-def normalize_sweep_simple(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
-    """Simple element-wise raw/calib normalization."""
-    width = min(raw.size, calib.size)
-    if width <= 0:
-        return raw
-    out = np.full_like(raw, np.nan, dtype=np.float32)
-    with np.errstate(divide="ignore", invalid="ignore"):
-        out[:width] = raw[:width] / calib[:width]
-    out = np.nan_to_num(out, nan=np.nan, posinf=np.nan, neginf=np.nan)
-    return out
-
-
-def build_calib_envelopes(calib: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
-    """Estimate smooth lower/upper envelopes from local extrema."""
-    n = int(calib.size)
-    if n <= 0:
-        empty = np.zeros((0,), dtype=np.float32)
-        return empty, empty
-
-    values = np.asarray(calib, dtype=np.float32)
-    finite = np.isfinite(values)
-    if not np.any(finite):
-        zeros = np.zeros_like(values, dtype=np.float32)
-        return zeros, zeros
-
-    if not np.all(finite):
-        x = np.arange(n, dtype=np.float32)
-        values = values.copy()
-        values[~finite] = np.interp(x[~finite], x[finite], values[finite]).astype(np.float32)
-
-    if n < 3:
-        return values.copy(), values.copy()
-
-    x = np.arange(n, dtype=np.float32)
-
-    def _moving_average(series: np.ndarray, window: int) -> np.ndarray:
-        width = max(1, int(window))
-        if width <= 1 or series.size <= 2:
-            return np.asarray(series, dtype=np.float32).copy()
-        if width % 2 == 0:
-            width += 1
-        pad = width // 2
-        padded = np.pad(np.asarray(series, dtype=np.float32), (pad, pad), mode="edge")
-        kernel = np.full((width,), 1.0 / float(width), dtype=np.float32)
-        return np.convolve(padded, kernel, mode="valid").astype(np.float32)
-
-    def _smooth_extrema_envelope(use_max: bool) -> np.ndarray:
-        step = max(3, n // 32)
-        node_idx_list = []
-        for start in range(0, n, step):
-            stop = min(n, start + step)
-            segment = values[start:stop]
-            idx_rel = int(np.argmax(segment) if use_max else np.argmin(segment))
-            node_idx_list.append(start + idx_rel)
-
-        extrema_idx = np.unique(np.asarray(node_idx_list, dtype=np.int64))
-        if extrema_idx.size == 0:
-            extrema_idx = np.asarray([int(np.argmax(values) if use_max else np.argmin(values))], dtype=np.int64)
-
-        node_idx = np.unique(np.concatenate(([0], extrema_idx, [n - 1]))).astype(np.int64)
-        node_vals = values[node_idx].astype(np.float32, copy=True)
-        node_vals[0] = float(values[extrema_idx[0]])
-        node_vals[-1] = float(values[extrema_idx[-1]])
-        node_vals = _moving_average(node_vals, 3)
-        node_vals[0] = float(values[extrema_idx[0]])
-        node_vals[-1] = float(values[extrema_idx[-1]])
-
-        envelope = np.interp(x, node_idx.astype(np.float32), node_vals).astype(np.float32)
-        smooth_window = max(1, n // 64)
-        if smooth_window > 1:
-            envelope = _moving_average(envelope, smooth_window)
-        return envelope
-
-    upper = _smooth_extrema_envelope(use_max=True)
-    lower = _smooth_extrema_envelope(use_max=False)
-
-    swap = lower > upper
-    if np.any(swap):
-        tmp = upper[swap].copy()
-        upper[swap] = lower[swap]
-        lower[swap] = tmp
-
-    return lower, upper
-
-
-def normalize_sweep_projector(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
-    """Project raw values between calibration envelopes into [-1000, 1000]."""
-    width = min(raw.size, calib.size)
-    if width <= 0:
-        return raw
-
-    out = np.full_like(raw, np.nan, dtype=np.float32)
-    raw_seg = np.asarray(raw[:width], dtype=np.float32)
-    lower, upper = build_calib_envelopes(np.asarray(calib[:width], dtype=np.float32))
-    span = upper - lower
-
-    finite_span = span[np.isfinite(span) & (span > 0)]
-    if finite_span.size > 0:
-        eps = max(float(np.median(finite_span)) * 1e-6, 1e-9)
-    else:
-        eps = 1e-9
-
-    valid = (
-        np.isfinite(raw_seg)
-        & np.isfinite(lower)
-        & np.isfinite(upper)
-        & (span > eps)
-    )
-    if np.any(valid):
-        proj = np.empty_like(raw_seg, dtype=np.float32)
-        proj[valid] = ((2.0 * (raw_seg[valid] - lower[valid]) / span[valid]) - 1.0) * 1000.0
-        proj[valid] = np.clip(proj[valid], -1000.0, 1000.0)
-        proj[~valid] = np.nan
-        out[:width] = proj
-    return out
-
-
-def resample_envelope(envelope: np.ndarray, width: int) -> np.ndarray:
-    """Resample an envelope to the target sweep width on the index axis."""
-    target_width = int(width)
-    if target_width <= 0:
-        return np.zeros((0,), dtype=np.float32)
-
-    values = np.asarray(envelope, dtype=np.float32).reshape(-1)
-    if values.size == 0:
-        return np.full((target_width,), np.nan, dtype=np.float32)
-    if values.size == target_width:
-        return values.astype(np.float32, copy=True)
-
-    x_src = np.arange(values.size, dtype=np.float32)
-    finite = np.isfinite(values)
-    if not np.any(finite):
-        return np.full((target_width,), np.nan, dtype=np.float32)
-    if int(np.count_nonzero(finite)) == 1:
-        fill = float(values[finite][0])
-        return np.full((target_width,), fill, dtype=np.float32)
-
-    x_dst = np.linspace(0.0, float(values.size - 1), target_width, dtype=np.float32)
-    return np.interp(x_dst, x_src[finite], values[finite]).astype(np.float32)
-
-
-def normalize_by_envelope(raw: np.ndarray, envelope: np.ndarray) -> np.ndarray:
-    """Normalize a sweep by an envelope with safe resampling and zero protection."""
-    raw_arr = np.asarray(raw, dtype=np.float32).reshape(-1)
-    if raw_arr.size == 0:
-        return raw_arr.copy()
-
-    env = resample_envelope(envelope, raw_arr.size)
-    out = np.full_like(raw_arr, np.nan, dtype=np.float32)
-    den_eps = np.float32(1e-9)
-    valid = np.isfinite(raw_arr) & np.isfinite(env)
-    if np.any(valid):
-        with np.errstate(divide="ignore", invalid="ignore"):
-            denom = env[valid] + np.where(env[valid] >= 0.0, den_eps, -den_eps)
-            out[valid] = raw_arr[valid] / denom
-    return np.nan_to_num(out, nan=np.nan, posinf=np.nan, neginf=np.nan)
-
-
-def normalize_by_calib(raw: np.ndarray, calib: np.ndarray, norm_type: str) -> np.ndarray:
-    """Apply the selected normalization method."""
-    norm = str(norm_type).strip().lower()
-    if norm == "simple":
-        return normalize_sweep_simple(raw, calib)
-    return normalize_sweep_projector(raw, calib)

rfg_adc_plotter/processing/normalizer.py

@@ -0,0 +1,149 @@
+"""Алгоритмы нормировки свипов по калибровочной кривой."""
+
+from typing import Tuple
+
+import numpy as np
+
+
+def normalize_simple(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
+    """Простая нормировка: поэлементное деление raw/calib."""
+    w = min(raw.size, calib.size)
+    if w <= 0:
+        return raw
+    out = np.full_like(raw, np.nan, dtype=np.float32)
+    with np.errstate(divide="ignore", invalid="ignore"):
+        out[:w] = raw[:w] / calib[:w]
+    out = np.nan_to_num(out, nan=np.nan, posinf=np.nan, neginf=np.nan)
+    return out
+
+
+def build_calib_envelopes(calib: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """Оценить огибающую по модулю сигнала.
+
+    Возвращает (lower, upper) = (-envelope, +envelope), где envelope —
+    интерполяция через локальные максимумы |calib|.
+    """
+    n = int(calib.size)
+    if n <= 0:
+        empty = np.zeros((0,), dtype=np.float32)
+        return empty, empty
+
+    y = np.asarray(calib, dtype=np.float32)
+    finite = np.isfinite(y)
+    if not np.any(finite):
+        zeros = np.zeros_like(y, dtype=np.float32)
+        return zeros, zeros
+
+    if not np.all(finite):
+        x = np.arange(n, dtype=np.float32)
+        y = y.copy()
+        y[~finite] = np.interp(x[~finite], x[finite], y[finite]).astype(np.float32)
+
+    a = np.abs(y)
+
+    if n < 3:
+        env = a.copy()
+        return -env, env
+
+    da = np.diff(a)
+    s = np.sign(da).astype(np.int8, copy=False)
+
+    if np.any(s == 0):
+        for i in range(1, s.size):
+            if s[i] == 0:
+                s[i] = s[i - 1]
+        for i in range(s.size - 2, -1, -1):
+            if s[i] == 0:
+                s[i] = s[i + 1]
+        s[s == 0] = 1
+
+    max_idx = np.where((s[:-1] > 0) & (s[1:] < 0))[0] + 1
+
+    x = np.arange(n, dtype=np.float32)
+
+    if max_idx.size == 0:
+        idx = np.array([0, n - 1], dtype=np.int64)
+    else:
+        idx = np.unique(np.concatenate(([0], max_idx, [n - 1]))).astype(np.int64)
+    env = np.interp(x, idx.astype(np.float32), a[idx]).astype(np.float32)
+
+    return -env, env
+
+
+def normalize_projector(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
+    """Нормировка через проекцию между огибающими калибровки в диапазон [-1000, +1000]."""
+    w = min(raw.size, calib.size)
+    if w <= 0:
+        return raw
+
+    out = np.full_like(raw, np.nan, dtype=np.float32)
+    raw_seg = np.asarray(raw[:w], dtype=np.float32)
+    lower, upper = build_calib_envelopes(np.asarray(calib[:w], dtype=np.float32))
+    span = upper - lower
+
+    finite_span = span[np.isfinite(span) & (span > 0)]
+    if finite_span.size > 0:
+        eps = max(float(np.median(finite_span)) * 1e-6, 1e-9)
+    else:
+        eps = 1e-9
+
+    valid = (
+        np.isfinite(raw_seg)
+        & np.isfinite(lower)
+        & np.isfinite(upper)
+        & (span > eps)
+    )
+    if np.any(valid):
+        proj = np.empty_like(raw_seg, dtype=np.float32)
+        proj[valid] = ((2.0 * (raw_seg[valid] - lower[valid]) / span[valid]) - 1.0) * 1000.0
+        proj[valid] = np.clip(proj[valid], -1000.0, 1000.0)
+        proj[~valid] = np.nan
+        out[:w] = proj
+
+    return out
+
+
+def normalize_by_calib(raw: np.ndarray, calib: np.ndarray, norm_type: str) -> np.ndarray:
+    """Нормировка свипа по выбранному алгоритму."""
+    nt = str(norm_type).strip().lower()
+    if nt == "simple":
+        return normalize_simple(raw, calib)
+    return normalize_projector(raw, calib)
+
+
+def normalize_by_envelope(raw: np.ndarray, envelope: np.ndarray) -> np.ndarray:
+    """Нормировка свипа через проекцию на огибающую из файла.
+
+    Воспроизводит логику normalize_projector: проецирует raw в [-1000, +1000]
+    используя готовую верхнюю огибающую (upper = envelope, lower = -envelope).
+    """
+    w = min(raw.size, envelope.size)
+    if w <= 0:
+        return raw
+
+    out = np.full_like(raw, np.nan, dtype=np.float32)
+    raw_seg = np.asarray(raw[:w], dtype=np.float32)
+    upper = np.asarray(envelope[:w], dtype=np.float32)
+    lower = -upper
+    span = upper - lower  # = 2 * upper
+
+    finite_span = span[np.isfinite(span) & (span > 0)]
+    if finite_span.size > 0:
+        eps = max(float(np.median(finite_span)) * 1e-6, 1e-9)
+    else:
+        eps = 1e-9
+
+    valid = (
+        np.isfinite(raw_seg)
+        & np.isfinite(lower)
+        & np.isfinite(upper)
+        & (span > eps)
+    )
+    if np.any(valid):
+        proj = np.empty_like(raw_seg, dtype=np.float32)
+        proj[valid] = ((2.0 * (raw_seg[valid] - lower[valid]) / span[valid]) - 1.0) * 1000.0
+        proj[valid] = np.clip(proj[valid], -1000.0, 1000.0)
+        proj[~valid] = np.nan
+        out[:w] = proj
+
+    return out

rfg_adc_plotter/processing/peaks.py

@@ -1,209 +0,0 @@
-"""Peak-search helpers for FFT visualizations."""
-
-from __future__ import annotations
-
-from typing import Dict, List, Optional
-
-import numpy as np
-
-
-def find_peak_width_markers(xs: np.ndarray, ys: np.ndarray) -> Optional[Dict[str, float]]:
-    """Find the dominant non-zero peak and its half-height width."""
-    x_arr = np.asarray(xs, dtype=np.float64)
-    y_arr = np.asarray(ys, dtype=np.float64)
-    valid = np.isfinite(x_arr) & np.isfinite(y_arr) & (x_arr > 0.0)
-    if int(np.count_nonzero(valid)) < 3:
-        return None
-
-    x = x_arr[valid]
-    y = y_arr[valid]
-    x_min = float(x[0])
-    x_max = float(x[-1])
-    x_span = x_max - x_min
-    central_mask = (x >= (x_min + 0.25 * x_span)) & (x <= (x_min + 0.75 * x_span))
-    if int(np.count_nonzero(central_mask)) > 0:
-        central_idx = np.flatnonzero(central_mask)
-        peak_idx = int(central_idx[int(np.argmax(y[central_mask]))])
-    else:
-        peak_idx = int(np.argmax(y))
-    peak_y = float(y[peak_idx])
-    shoulder_gap = max(1, min(8, y.size // 64 if y.size > 0 else 1))
-    shoulder_width = max(4, min(32, y.size // 16 if y.size > 0 else 4))
-    left_lo = max(0, peak_idx - shoulder_gap - shoulder_width)
-    left_hi = max(0, peak_idx - shoulder_gap)
-    right_lo = min(y.size, peak_idx + shoulder_gap + 1)
-    right_hi = min(y.size, right_lo + shoulder_width)
-    background_parts = []
-    if left_hi > left_lo:
-        background_parts.append(float(np.nanmedian(y[left_lo:left_hi])))
-    if right_hi > right_lo:
-        background_parts.append(float(np.nanmedian(y[right_lo:right_hi])))
-    if background_parts:
-        background = float(np.mean(background_parts))
-    else:
-        background = float(np.nanpercentile(y, 10))
-    if not np.isfinite(peak_y) or not np.isfinite(background) or peak_y <= background:
-        return None
-
-    half_level = background + 0.5 * (peak_y - background)
-
-    def _interp_cross(x0: float, y0: float, x1: float, y1: float) -> float:
-        if not (np.isfinite(x0) and np.isfinite(y0) and np.isfinite(x1) and np.isfinite(y1)):
-            return x1
-        dy = y1 - y0
-        if dy == 0.0:
-            return x1
-        t = (half_level - y0) / dy
-        t = min(1.0, max(0.0, t))
-        return x0 + t * (x1 - x0)
-
-    left_x = float(x[0])
-    for i in range(peak_idx, 0, -1):
-        if y[i - 1] <= half_level <= y[i]:
-            left_x = _interp_cross(float(x[i - 1]), float(y[i - 1]), float(x[i]), float(y[i]))
-            break
-
-    right_x = float(x[-1])
-    for i in range(peak_idx, x.size - 1):
-        if y[i] >= half_level >= y[i + 1]:
-            right_x = _interp_cross(float(x[i]), float(y[i]), float(x[i + 1]), float(y[i + 1]))
-            break
-
-    width = right_x - left_x
-    if not np.isfinite(width) or width <= 0.0:
-        return None
-
-    return {
-        "background": background,
-        "left": left_x,
-        "right": right_x,
-        "width": width,
-        "amplitude": peak_y,
-    }
-
-
-def rolling_median_ref(xs: np.ndarray, ys: np.ndarray, window_ghz: float) -> np.ndarray:
-    """Compute a rolling median reference on a fixed-width X window."""
-    x = np.asarray(xs, dtype=np.float64)
-    y = np.asarray(ys, dtype=np.float64)
-    out = np.full(y.shape, np.nan, dtype=np.float64)
-    if x.size == 0 or y.size == 0 or x.size != y.size:
-        return out
-    width = float(window_ghz)
-    if not np.isfinite(width) or width <= 0.0:
-        return out
-    half = 0.5 * width
-    for i in range(x.size):
-        xi = x[i]
-        if not np.isfinite(xi):
-            continue
-        left = np.searchsorted(x, xi - half, side="left")
-        right = np.searchsorted(x, xi + half, side="right")
-        if right <= left:
-            continue
-        segment = y[left:right]
-        finite = np.isfinite(segment)
-        if not np.any(finite):
-            continue
-        out[i] = float(np.nanmedian(segment))
-    return out
-
-
-def find_top_peaks_over_ref(
-    xs: np.ndarray,
-    ys: np.ndarray,
-    ref: np.ndarray,
-    top_n: int = 3,
-) -> List[Dict[str, float]]:
-    """Find the top-N non-overlapping peaks above a reference curve."""
-    x = np.asarray(xs, dtype=np.float64)
-    y = np.asarray(ys, dtype=np.float64)
-    r = np.asarray(ref, dtype=np.float64)
-    if x.size < 3 or y.size != x.size or r.size != x.size:
-        return []
-
-    valid = np.isfinite(x) & np.isfinite(y) & np.isfinite(r)
-    if not np.any(valid):
-        return []
-    delta = np.full_like(y, np.nan, dtype=np.float64)
-    delta[valid] = y[valid] - r[valid]
-
-    candidates: List[int] = []
-    for i in range(1, x.size - 1):
-        if not (np.isfinite(delta[i - 1]) and np.isfinite(delta[i]) and np.isfinite(delta[i + 1])):
-            continue
-        if delta[i] <= 0.0:
-            continue
-        left_ok = delta[i] > delta[i - 1]
-        right_ok = delta[i] >= delta[i + 1]
-        alt_left_ok = delta[i] >= delta[i - 1]
-        alt_right_ok = delta[i] > delta[i + 1]
-        if (left_ok and right_ok) or (alt_left_ok and alt_right_ok):
-            candidates.append(i)
-    if not candidates:
-        return []
-
-    candidates.sort(key=lambda i: float(delta[i]), reverse=True)
-
-    def _interp_cross(x0: float, y0: float, x1: float, y1: float, y_cross: float) -> float:
-        dy = y1 - y0
-        if not np.isfinite(dy) or dy == 0.0:
-            return x1
-        t = (y_cross - y0) / dy
-        t = min(1.0, max(0.0, t))
-        return x0 + t * (x1 - x0)
-
-    picked: List[Dict[str, float]] = []
-    for idx in candidates:
-        peak_y = float(y[idx])
-        peak_ref = float(r[idx])
-        peak_h = float(delta[idx])
-        if not (np.isfinite(peak_y) and np.isfinite(peak_ref) and np.isfinite(peak_h)) or peak_h <= 0.0:
-            continue
-
-        half_level = peak_ref + 0.5 * peak_h
-
-        left_x = float(x[0])
-        for i in range(idx, 0, -1):
-            y0 = float(y[i - 1])
-            y1 = float(y[i])
-            if np.isfinite(y0) and np.isfinite(y1) and (y0 <= half_level <= y1):
-                left_x = _interp_cross(float(x[i - 1]), y0, float(x[i]), y1, half_level)
-                break
-
-        right_x = float(x[-1])
-        for i in range(idx, x.size - 1):
-            y0 = float(y[i])
-            y1 = float(y[i + 1])
-            if np.isfinite(y0) and np.isfinite(y1) and (y0 >= half_level >= y1):
-                right_x = _interp_cross(float(x[i]), y0, float(x[i + 1]), y1, half_level)
-                break
-
-        width = float(right_x - left_x)
-        if not np.isfinite(width) or width <= 0.0:
-            continue
-
-        overlap = False
-        for peak in picked:
-            if not (right_x <= peak["left"] or left_x >= peak["right"]):
-                overlap = True
-                break
-        if overlap:
-            continue
-
-        picked.append(
-            {
-                "x": float(x[idx]),
-                "peak_y": peak_y,
-                "ref": peak_ref,
-                "height": peak_h,
-                "left": left_x,
-                "right": right_x,
-                "width": width,
-            }
-        )
-        if len(picked) >= int(max(1, top_n)):
-            break
-
-    picked.sort(key=lambda peak: peak["x"])
-    return picked

rfg_adc_plotter/state/__init__.py

@@ -1,7 +0,0 @@
-"""Runtime state helpers."""
-
-from rfg_adc_plotter.state.background_buffer import BackgroundMedianBuffer
-from rfg_adc_plotter.state.ring_buffer import RingBuffer
-from rfg_adc_plotter.state.runtime_state import RuntimeState
-
-__all__ = ["BackgroundMedianBuffer", "RingBuffer", "RuntimeState"]

rfg_adc_plotter/state/app_state.py

@@ -0,0 +1,227 @@
+"""Состояние приложения: текущие свипы и настройки калибровки/нормировки."""
+
+import os
+from queue import Empty, Queue
+from typing import Any, Dict, Mapping, Optional
+
+import numpy as np
+
+from rfg_adc_plotter.processing.normalizer import (
+    build_calib_envelopes,
+    normalize_by_calib,
+    normalize_by_envelope,
+)
+from rfg_adc_plotter.state.ring_buffer import RingBuffer
+from rfg_adc_plotter.types import SweepInfo, SweepPacket
+
+CALIB_ENVELOPE_PATH = "calib_envelope.npy"
+BACKGROUND_PATH = "background.npy"
+
+
+def format_status(data: Mapping[str, Any]) -> str:
+    """Преобразовать словарь метрик в одну строку 'k:v'."""
+
+    def _fmt(v: Any) -> str:
+        if v is None:
+            return "NA"
+        try:
+            fv = float(v)
+        except Exception:
+            return str(v)
+        if not np.isfinite(fv):
+            return "nan"
+        if abs(fv) >= 1000 or (0 < abs(fv) < 0.01):
+            return f"{fv:.3g}"
+        return f"{fv:.3f}".rstrip("0").rstrip(".")
+
+    parts = [f"{k}:{_fmt(v)}" for k, v in data.items()]
+    return "  ".join(parts)
+
+
+class AppState:
+    """Весь изменяемый GUI-state: текущие данные, калибровка, настройки.
+
+    Методы drain_queue и set_calib_enabled заменяют одноимённые closures
+    с nonlocal из оригинального кода.
+    """
+
+    def __init__(self, norm_type: str = "projector"):
+        self.current_sweep_raw: Optional[np.ndarray] = None
+        self.current_sweep_norm: Optional[np.ndarray] = None
+        self.last_calib_sweep: Optional[np.ndarray] = None
+        self.current_info: Optional[SweepInfo] = None
+        self.calib_enabled: bool = False
+        self.norm_type: str = norm_type
+        # "live" — нормировка по текущему ch0-свипу; "file" — по огибающей из файла
+        self.calib_mode: str = "live"
+        self.calib_file_envelope: Optional[np.ndarray] = None
+        # Вычет фона
+        self.background: Optional[np.ndarray] = None
+        self.background_enabled: bool = False
+        self._last_sweep_for_ring: Optional[np.ndarray] = None
+
+    def _normalize(self, raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
+        if self.calib_mode == "file" and self.calib_file_envelope is not None:
+            return normalize_by_envelope(raw, self.calib_file_envelope)
+        return normalize_by_calib(raw, calib, self.norm_type)
+
+    def save_calib_envelope(self, path: str = CALIB_ENVELOPE_PATH) -> bool:
+        """Вычислить огибающую из last_calib_sweep и сохранить в файл.
+
+        Возвращает True при успехе.
+        """
+        if self.last_calib_sweep is None:
+            return False
+        try:
+            _lower, upper = build_calib_envelopes(self.last_calib_sweep)
+            np.save(path, upper)
+            return True
+        except Exception as exc:
+            import sys
+            sys.stderr.write(f"[warn] Не удалось сохранить огибающую: {exc}\n")
+            return False
+
+    def load_calib_envelope(self, path: str = CALIB_ENVELOPE_PATH) -> bool:
+        """Загрузить огибающую из файла.
+
+        Возвращает True при успехе.
+        """
+        if not os.path.isfile(path):
+            return False
+        try:
+            env = np.load(path)
+            self.calib_file_envelope = np.asarray(env, dtype=np.float32)
+            return True
+        except Exception as exc:
+            import sys
+            sys.stderr.write(f"[warn] Не удалось загрузить огибающую: {exc}\n")
+            return False
+
+    def set_calib_mode(self, mode: str):
+        """Переключить режим калибровки: 'live' или 'file'."""
+        self.calib_mode = mode
+
+    def save_background(self, path: str = BACKGROUND_PATH) -> bool:
+        """Сохранить текущий sweep_for_ring как фоновый спектр.
+
+        Сохраняет последний свип, который был записан в ринг-буфер
+        (нормированный, если калибровка включена, иначе сырой).
+        Возвращает True при успехе.
+        """
+        if self._last_sweep_for_ring is None:
+            return False
+        try:
+            np.save(path, self._last_sweep_for_ring)
+            return True
+        except Exception as exc:
+            import sys
+            sys.stderr.write(f"[warn] Не удалось сохранить фон: {exc}\n")
+            return False
+
+    def load_background(self, path: str = BACKGROUND_PATH) -> bool:
+        """Загрузить фоновый спектр из файла.
+
+        Возвращает True при успехе.
+        """
+        if not os.path.isfile(path):
+            return False
+        try:
+            bg = np.load(path)
+            self.background = np.asarray(bg, dtype=np.float32)
+            return True
+        except Exception as exc:
+            import sys
+            sys.stderr.write(f"[warn] Не удалось загрузить фон: {exc}\n")
+            return False
+
+    def set_background_enabled(self, enabled: bool):
+        """Включить/выключить вычет фона."""
+        self.background_enabled = enabled
+
+    def set_calib_enabled(self, enabled: bool):
+        """Включить/выключить режим калибровки, пересчитать norm-свип."""
+        self.calib_enabled = enabled
+        if self.calib_enabled and self.current_sweep_raw is not None:
+            if self.calib_mode == "file" and self.calib_file_envelope is not None:
+                self.current_sweep_norm = normalize_by_envelope(
+                    self.current_sweep_raw, self.calib_file_envelope
+                )
+            elif self.calib_mode == "live" and self.last_calib_sweep is not None:
+                self.current_sweep_norm = self._normalize(
+                    self.current_sweep_raw, self.last_calib_sweep
+                )
+            else:
+                self.current_sweep_norm = None
+        else:
+            self.current_sweep_norm = None
+
+    def drain_queue(self, q: "Queue[SweepPacket]", ring: RingBuffer) -> int:
+        """Вытащить все ожидающие свипы из очереди, обновить state и ring.
+
+        Возвращает количество обработанных свипов.
+        """
+        drained = 0
+        while True:
+            try:
+                s, info = q.get_nowait()
+            except Empty:
+                break
+            drained += 1
+            self.current_sweep_raw = s
+            self.current_info = info
+
+            ch = 0
+            try:
+                ch = int(info.get("ch", 0)) if isinstance(info, dict) else 0
+            except Exception:
+                ch = 0
+
+            # Канал 0 — опорный (калибровочный) свип
+            if ch == 0:
+                self.last_calib_sweep = s
+                self.save_calib_envelope()
+                self.current_sweep_norm = None
+                sweep_for_ring = s
+                self._last_sweep_for_ring = sweep_for_ring
+            else:
+                can_normalize = self.calib_enabled and (
+                    (self.calib_mode == "file" and self.calib_file_envelope is not None)
+                    or (self.calib_mode == "live" and self.last_calib_sweep is not None)
+                )
+                if can_normalize:
+                    calib_ref = self.last_calib_sweep if self.last_calib_sweep is not None else s
+                    self.current_sweep_norm = self._normalize(s, calib_ref)
+                    sweep_for_ring = self.current_sweep_norm
+                else:
+                    self.current_sweep_norm = None
+                    sweep_for_ring = s
+
+            # Вычет фона (в том же домене что и sweep_for_ring)
+            if self.background_enabled and self.background is not None and ch != 0:
+                w = min(sweep_for_ring.size, self.background.size)
+                sweep_for_ring = sweep_for_ring.copy()
+                sweep_for_ring[:w] -= self.background[:w]
+                self.current_sweep_norm = sweep_for_ring
+
+            self._last_sweep_for_ring = sweep_for_ring
+            ring.ensure_init(s.size)
+            ring.push(sweep_for_ring)
+        return drained
+
+    def format_channel_label(self) -> str:
+        """Строка с номерами каналов для подписи на графике."""
+        if self.current_info is None:
+            return ""
+        info = self.current_info
+        chs = info.get("chs") if isinstance(info, dict) else None
+        if chs is None:
+            chs = info.get("ch") if isinstance(info, dict) else None
+        if chs is None:
+            return ""
+        try:
+            if isinstance(chs, (list, tuple, set)):
+                ch_list = sorted(int(v) for v in chs)
+                return "chs " + ", ".join(str(v) for v in ch_list)
+            return f"chs {int(chs)}"
+        except Exception:
+            return f"chs {chs}"

rfg_adc_plotter/state/background_buffer.py

@@ -1,49 +0,0 @@
-"""Rolling median buffer for persisted FFT background capture."""
-
-from __future__ import annotations
-
-from typing import Optional
-
-import numpy as np
-
-
-class BackgroundMedianBuffer:
-    """Store recent FFT rows and expose their median profile."""
-
-    def __init__(self, max_rows: int):
-        self.max_rows = max(1, int(max_rows))
-        self.width = 0
-        self.head = 0
-        self.count = 0
-        self.rows: Optional[np.ndarray] = None
-
-    def reset(self) -> None:
-        self.width = 0
-        self.head = 0
-        self.count = 0
-        self.rows = None
-
-    def push(self, fft_mag: np.ndarray) -> None:
-        values = np.asarray(fft_mag, dtype=np.float32).reshape(-1)
-        if values.size == 0:
-            return
-        if self.rows is None or self.width != values.size:
-            self.width = values.size
-            self.rows = np.full((self.max_rows, self.width), np.nan, dtype=np.float32)
-            self.head = 0
-            self.count = 0
-        self.rows[self.head, :] = values
-        self.head = (self.head + 1) % self.max_rows
-        self.count = min(self.count + 1, self.max_rows)
-
-    def median(self) -> Optional[np.ndarray]:
-        if self.rows is None or self.count <= 0:
-            return None
-        rows = self.rows[: self.count] if self.count < self.max_rows else self.rows
-        valid_rows = np.any(np.isfinite(rows), axis=1)
-        if not np.any(valid_rows):
-            return None
-        median = np.nanmedian(rows[valid_rows], axis=0).astype(np.float32, copy=False)
-        if not np.any(np.isfinite(median)):
-            return None
-        return np.nan_to_num(median, nan=0.0).astype(np.float32, copy=False)

rfg_adc_plotter/state/ring_buffer.py

@@ -1,193 +1,187 @@
-"""Ring buffers for raw sweeps and FFT waterfall rows."""
-
-from __future__ import annotations
+"""Кольцевой буфер свипов и FFT-строк для водопадного отображения."""
 
 import time
-from typing import Optional
+from typing import Optional, Tuple
 
 import numpy as np
 
-from rfg_adc_plotter.constants import FFT_LEN, SWEEP_FREQ_MAX_GHZ, SWEEP_FREQ_MIN_GHZ, WF_WIDTH
-from rfg_adc_plotter.processing.fft import compute_distance_axis, compute_fft_mag_row, fft_mag_to_db
+from rfg_adc_plotter.constants import (
+    FFT_LEN,
+    FREQ_SPAN_GHZ,
+    IFFT_LEN,
+    SWEEP_LEN,
+    WF_WIDTH,
+    ZEROS_LOW,
+    ZEROS_MID,
+)
 
 
 class RingBuffer:
-    """Store raw sweeps, FFT rows, and matching time markers."""
+    """Хранит последние N свипов и соответствующие FFT-строки.
+
+    Все мутабельные поля водопада инкапсулированы здесь,
+    что устраняет необходимость nonlocal в GUI-коде.
+    """
 
     def __init__(self, max_sweeps: int):
-        self.max_sweeps = int(max_sweeps)
-        self.fft_bins = FFT_LEN // 2 + 1
-        self.fft_mode = "symmetric"
-        self.width = 0
-        self.head = 0
-        self.ring: Optional[np.ndarray] = None
-        self.ring_time: Optional[np.ndarray] = None
-        self.ring_fft: Optional[np.ndarray] = None
+        self.max_sweeps = max_sweeps
+        self.fft_bins = IFFT_LEN  # = 1953 (полная длина IFFT-результата)
+
+        # Инициализируются при первом свипе (ensure_init)
+        self.ring: Optional[np.ndarray] = None        # (max_sweeps, WF_WIDTH)
+        self.ring_fft: Optional[np.ndarray] = None    # (max_sweeps, fft_bins)
+        self.ring_time: Optional[np.ndarray] = None   # (max_sweeps,)
+        self.head: int = 0
+        self.width: Optional[int] = None
         self.x_shared: Optional[np.ndarray] = None
-        self.distance_axis: Optional[np.ndarray] = None
-        self.last_fft_mag: Optional[np.ndarray] = None
-        self.last_fft_db: Optional[np.ndarray] = None
-        self.last_freqs: Optional[np.ndarray] = None
+        self.fft_time_axis: Optional[np.ndarray] = None  # временная ось IFFT в нс
         self.y_min_fft: Optional[float] = None
         self.y_max_fft: Optional[float] = None
+        # FFT последнего свипа (для отображения без повторного вычисления)
+        self.last_fft_vals: Optional[np.ndarray] = None
 
     @property
     def is_ready(self) -> bool:
-        return self.ring is not None and self.ring_fft is not None
+        return self.ring is not None
 
-    @property
-    def fft_symmetric(self) -> bool:
-        return self.fft_mode == "symmetric"
-
-    def reset(self) -> None:
-        """Drop all buffered sweeps and derived FFT state."""
-        self.width = 0
-        self.head = 0
-        self.ring = None
-        self.ring_time = None
-        self.ring_fft = None
-        self.x_shared = None
-        self.distance_axis = None
-        self.last_fft_mag = None
-        self.last_fft_db = None
-        self.last_freqs = None
-        self.y_min_fft = None
-        self.y_max_fft = None
-
-    def ensure_init(self, sweep_width: int) -> bool:
-        """Allocate or resize buffers. Returns True when geometry changed."""
-        target_width = max(int(sweep_width), int(WF_WIDTH))
-        changed = False
-        if self.ring is None or self.ring_time is None or self.ring_fft is None:
-            self.width = target_width
+    def ensure_init(self, sweep_width: int):
+        """Инициализировать буферы при первом свипе. Повторные вызовы — no-op (кроме x_shared)."""
+        if self.ring is None:
+            self.width = WF_WIDTH
             self.ring = np.full((self.max_sweeps, self.width), np.nan, dtype=np.float32)
             self.ring_time = np.full((self.max_sweeps,), np.nan, dtype=np.float64)
             self.ring_fft = np.full((self.max_sweeps, self.fft_bins), np.nan, dtype=np.float32)
+            # Временная ось IFFT: шаг dt = 1/(FREQ_SPAN_GHZ*1e9), переведём в нс
+            self.fft_time_axis = (
+                np.arange(IFFT_LEN, dtype=np.float64) / (FREQ_SPAN_GHZ * 1e9) * 1e9
+            ).astype(np.float32)
             self.head = 0
-            changed = True
-        elif target_width != self.width:
-            new_ring = np.full((self.max_sweeps, target_width), np.nan, dtype=np.float32)
-            take = min(self.width, target_width)
-            new_ring[:, :take] = self.ring[:, :take]
-            self.ring = new_ring
-            self.width = target_width
-            changed = True
+        # Обновляем x_shared если пришёл свип большего размера
+        if self.x_shared is None or sweep_width > self.x_shared.size:
+            self.x_shared = np.linspace(3.323, 14.323, sweep_width, dtype=np.float32)
 
-        if self.x_shared is None or self.x_shared.size != self.width:
-            self.x_shared = np.linspace(
-                SWEEP_FREQ_MIN_GHZ,
-                SWEEP_FREQ_MAX_GHZ,
-                self.width,
-                dtype=np.float32,
-            )
-            changed = True
-        return changed
-
-    def set_fft_mode(self, mode: str) -> bool:
-        """Switch FFT mode and rebuild cached FFT rows from stored sweeps."""
-        normalized_mode = str(mode).strip().lower()
-        if normalized_mode in {"ordinary", "normal"}:
-            normalized_mode = "direct"
-        if normalized_mode in {"sym", "mirror"}:
-            normalized_mode = "symmetric"
-        if normalized_mode in {"positive-centered", "positive_centered", "zero_left"}:
-            normalized_mode = "positive_only"
-        if normalized_mode not in {"direct", "symmetric", "positive_only"}:
-            raise ValueError(f"Unsupported FFT mode: {mode!r}")
-        if normalized_mode == self.fft_mode:
-            return False
-
-        self.fft_mode = normalized_mode
-        self.y_min_fft = None
-        self.y_max_fft = None
-
-        if self.ring is None or self.ring_fft is None:
-            return True
-
-        self.ring_fft.fill(np.nan)
-        for row_idx in range(self.ring.shape[0]):
-            sweep_row = self.ring[row_idx]
-            if not np.any(np.isfinite(sweep_row)):
-                continue
-            fft_mag = compute_fft_mag_row(
-                sweep_row,
-                self.last_freqs,
-                self.fft_bins,
-                mode=self.fft_mode,
-            )
-            self.ring_fft[row_idx, :] = fft_mag
-
-        if self.last_freqs is not None:
-            self.distance_axis = compute_distance_axis(
-                self.last_freqs,
-                self.fft_bins,
-                mode=self.fft_mode,
-            )
-
-        last_idx = (self.head - 1) % self.max_sweeps
-        if self.ring_fft.shape[0] > 0:
-            last_fft = self.ring_fft[last_idx]
-            self.last_fft_mag = np.asarray(last_fft, dtype=np.float32).copy()
-            self.last_fft_db = fft_mag_to_db(last_fft)
-            finite = self.ring_fft[np.isfinite(self.ring_fft)]
-            if finite.size > 0:
-                finite_db = fft_mag_to_db(finite.astype(np.float32, copy=False))
-                self.y_min_fft = float(np.nanmin(finite_db))
-                self.y_max_fft = float(np.nanmax(finite_db))
-        return True
-
-    def set_symmetric_fft_enabled(self, enabled: bool) -> bool:
-        """Backward-compatible wrapper for the old two-state FFT switch."""
-        return self.set_fft_mode("symmetric" if enabled else "direct")
-
-    def push(self, sweep: np.ndarray, freqs: Optional[np.ndarray] = None) -> None:
-        """Push a processed sweep and refresh raw/FFT buffers."""
-        if sweep is None or sweep.size == 0:
+    def push(self, s: np.ndarray):
+        """Добавить строку свипа в кольцевой буфер, вычислить FFT-строку."""
+        if s is None or s.size == 0 or self.ring is None:
             return
-        self.ensure_init(int(sweep.size))
-        if self.ring is None or self.ring_time is None or self.ring_fft is None:
-            return
-
-        row = np.full((self.width,), np.nan, dtype=np.float32)
-        take = min(self.width, int(sweep.size))
-        row[:take] = np.asarray(sweep[:take], dtype=np.float32)
+        w = self.ring.shape[1]
+        row = np.full((w,), np.nan, dtype=np.float32)
+        take = min(w, s.size)
+        row[:take] = s[:take]
         self.ring[self.head, :] = row
         self.ring_time[self.head] = time.time()
-        if freqs is not None:
-            self.last_freqs = np.asarray(freqs, dtype=np.float64).copy()
+        self.head = (self.head + 1) % self.ring.shape[0]
 
-        fft_mag = compute_fft_mag_row(sweep, freqs, self.fft_bins, mode=self.fft_mode)
-        self.ring_fft[self.head, :] = fft_mag
-        self.last_fft_mag = np.asarray(fft_mag, dtype=np.float32).copy()
-        self.last_fft_db = fft_mag_to_db(fft_mag)
+        self._push_fft(s)
 
-        if self.last_fft_db.size > 0:
-            fr_min = float(np.nanmin(self.last_fft_db))
-            fr_max = float(np.nanmax(self.last_fft_db))
-            self.y_min_fft = fr_min if self.y_min_fft is None else min(self.y_min_fft, fr_min)
-            self.y_max_fft = fr_max if self.y_max_fft is None else max(self.y_max_fft, fr_max)
+    def _push_fft(self, s: np.ndarray):
+        bins = self.ring_fft.shape[1]  # = IFFT_LEN = 1953
+        if s is None or s.size == 0:
+            fft_row = np.full((bins,), np.nan, dtype=np.float32)
+        else:
+            # 1. Взять первые SWEEP_LEN отсчётов (остаток — нули если свип короче)
+            sig = np.zeros(SWEEP_LEN, dtype=np.float32)
+            take = min(int(s.size), SWEEP_LEN)
+            seg = np.nan_to_num(s[:take], nan=0.0).astype(np.float32, copy=False)
+            sig[:take] = seg
 
-        self.distance_axis = compute_distance_axis(freqs, self.fft_bins, mode=self.fft_mode)
-        self.head = (self.head + 1) % self.max_sweeps
+            # 2. Собрать двусторонний спектр:
+            #    [ZEROS_LOW нулей | ZEROS_MID нулей | SWEEP_LEN данных]
+            #    = [-14.3..-3.2 ГГц | -3.2..+3.2 ГГц | +3.2..+14.3 ГГц]
+            data = np.zeros(IFFT_LEN, dtype=np.complex64)
+            data[ZEROS_LOW + ZEROS_MID:] = sig
 
-    def get_display_raw(self) -> np.ndarray:
+            # 3. ifftshift + ifft → временной профиль
+            spec = np.fft.ifftshift(data)
+            result = np.fft.ifft(spec)
+
+            # 4. Амплитуда в дБ
+            mag = np.abs(result).astype(np.float32)
+            fft_row = (20.0 * np.log10(mag + 1e-9)).astype(np.float32)
+
+        prev_head = (self.head - 1) % self.ring_fft.shape[0]
+        self.ring_fft[prev_head, :] = fft_row
+        self.last_fft_vals = fft_row
+
+        fr_min = np.nanmin(fft_row)
+        fr_max = float(np.nanpercentile(fft_row, 90))
+        if self.y_min_fft is None or (not np.isnan(fr_min) and fr_min < self.y_min_fft):
+            self.y_min_fft = float(fr_min)
+        if self.y_max_fft is None or (not np.isnan(fr_max) and fr_max > self.y_max_fft):
+            self.y_max_fft = float(fr_max)
+
+    def get_display_ring(self) -> np.ndarray:
+        """Кольцо в порядке от старого к новому, ось времени по X. Форма: (width, time)."""
         if self.ring is None:
             return np.zeros((1, 1), dtype=np.float32)
         base = self.ring if self.head == 0 else np.roll(self.ring, -self.head, axis=0)
-        return base.T
+        return base.T  # (width, time)
 
-    def get_display_fft_linear(self) -> np.ndarray:
+    def get_display_ring_fft(self) -> np.ndarray:
+        """FFT-кольцо в порядке от старого к новому. Форма: (bins, time)."""
         if self.ring_fft is None:
             return np.zeros((1, 1), dtype=np.float32)
         base = self.ring_fft if self.head == 0 else np.roll(self.ring_fft, -self.head, axis=0)
-        return base.T
-
-    def get_last_fft_linear(self) -> Optional[np.ndarray]:
-        if self.last_fft_mag is None:
-            return None
-        return np.asarray(self.last_fft_mag, dtype=np.float32).copy()
+        return base.T  # (bins, time)
 
     def get_display_times(self) -> Optional[np.ndarray]:
+        """Временные метки строк в порядке от старого к новому."""
         if self.ring_time is None:
             return None
         return self.ring_time if self.head == 0 else np.roll(self.ring_time, -self.head)
+
+    def subtract_recent_mean_fft(
+        self, disp_fft: np.ndarray, spec_mean_sec: float
+    ) -> np.ndarray:
+        """Вычесть среднее по каждой частоте за последние spec_mean_sec секунд."""
+        if spec_mean_sec <= 0.0:
+            return disp_fft
+        disp_times = self.get_display_times()
+        if disp_times is None:
+            return disp_fft
+        now_t = time.time()
+        mask = np.isfinite(disp_times) & (disp_times >= (now_t - spec_mean_sec))
+        if not np.any(mask):
+            return disp_fft
+        try:
+            mean_spec = np.nanmean(disp_fft[:, mask], axis=1)
+        except Exception:
+            return disp_fft
+        mean_spec = np.nan_to_num(mean_spec, nan=0.0)
+        return disp_fft - mean_spec[:, None]
+
+    def compute_fft_levels(
+        self, disp_fft: np.ndarray, spec_clip: Optional[Tuple[float, float]]
+    ) -> Optional[Tuple[float, float]]:
+        """Вычислить (vmin, vmax) для отображения водопада спектров."""
+        # 1. По среднему спектру за видимое время
+        try:
+            mean_spec = np.nanmean(disp_fft, axis=1)
+            vmin_v = float(np.nanmin(mean_spec))
+            vmax_v = float(np.nanmax(mean_spec))
+            if np.isfinite(vmin_v) and np.isfinite(vmax_v) and vmin_v != vmax_v:
+                return (vmin_v, vmax_v)
+        except Exception:
+            pass
+
+        # 2. Процентильная обрезка
+        if spec_clip is not None:
+            try:
+                vmin_v = float(np.nanpercentile(disp_fft, spec_clip[0]))
+                vmax_v = float(np.nanpercentile(disp_fft, spec_clip[1]))
+                if np.isfinite(vmin_v) and np.isfinite(vmax_v) and vmin_v != vmax_v:
+                    return (vmin_v, vmax_v)
+            except Exception:
+                pass
+
+        # 3. Глобальные накопленные мин/макс
+        if (
+            self.y_min_fft is not None
+            and self.y_max_fft is not None
+            and np.isfinite(self.y_min_fft)
+            and np.isfinite(self.y_max_fft)
+            and self.y_min_fft != self.y_max_fft
+        ):
+            return (self.y_min_fft, self.y_max_fft)
+
+        return None

rfg_adc_plotter/state/runtime_state.py

@@ -1,46 +0,0 @@
-"""Mutable state container for the PyQtGraph backend."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional
-
-import numpy as np
-
-from rfg_adc_plotter.constants import BACKGROUND_MEDIAN_SWEEPS
-from rfg_adc_plotter.state.background_buffer import BackgroundMedianBuffer
-from rfg_adc_plotter.state.ring_buffer import RingBuffer
-from rfg_adc_plotter.types import SweepAuxCurves, SweepInfo
-
-
-@dataclass
-class RuntimeState:
-    ring: RingBuffer
-    range_min_ghz: float = 0.0
-    range_max_ghz: float = 0.0
-    full_current_freqs: Optional[np.ndarray] = None
-    full_current_sweep_raw: Optional[np.ndarray] = None
-    full_current_aux_curves: SweepAuxCurves = None
-    current_freqs: Optional[np.ndarray] = None
-    current_distances: Optional[np.ndarray] = None
-    current_sweep_raw: Optional[np.ndarray] = None
-    current_aux_curves: SweepAuxCurves = None
-    current_sweep_norm: Optional[np.ndarray] = None
-    current_fft_mag: Optional[np.ndarray] = None
-    current_fft_db: Optional[np.ndarray] = None
-    last_calib_sweep: Optional[np.ndarray] = None
-    calib_envelope: Optional[np.ndarray] = None
-    calib_file_path: Optional[str] = None
-    background_buffer: BackgroundMedianBuffer = field(
-        default_factory=lambda: BackgroundMedianBuffer(BACKGROUND_MEDIAN_SWEEPS)
-    )
-    background_profile: Optional[np.ndarray] = None
-    background_file_path: Optional[str] = None
-    current_info: Optional[SweepInfo] = None
-    current_peak_width: Optional[float] = None
-    current_peak_amplitude: Optional[float] = None
-    peak_candidates: List[Dict[str, float]] = field(default_factory=list)
-    plot_dirty: bool = False
-
-    def mark_dirty(self) -> None:
-        self.plot_dirty = True

rfg_adc_plotter/types.py

@@ -1,31 +1,7 @@
-"""Shared runtime and parser types."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any, Dict, Optional, Tuple, TypeAlias, Union
+from typing import Any, Dict, Tuple, Union
 
 import numpy as np
 
-
 Number = Union[int, float]
 SweepInfo = Dict[str, Any]
-SweepData = Dict[str, np.ndarray]
-SweepAuxCurves = Optional[Tuple[np.ndarray, np.ndarray]]
-SweepPacket = Tuple[np.ndarray, SweepInfo, SweepAuxCurves]
-
-
-@dataclass(frozen=True)
-class StartEvent:
-    ch: Optional[int] = None
-
-
-@dataclass(frozen=True)
-class PointEvent:
-    ch: int
-    x: int
-    y: float
-    aux: Optional[Tuple[float, float]] = None
-
-
-ParserEvent: TypeAlias = Union[StartEvent, PointEvent]
+SweepPacket = Tuple[np.ndarray, SweepInfo]

tests/test_background_buffer.py

@@ -1,44 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-import unittest
-
-from rfg_adc_plotter.state.background_buffer import BackgroundMedianBuffer
-
-
-class BackgroundMedianBufferTests(unittest.TestCase):
-    def test_buffer_returns_median_for_partial_fill(self):
-        buffer = BackgroundMedianBuffer(max_rows=4)
-        buffer.push(np.asarray([1.0, 5.0, 9.0], dtype=np.float32))
-        buffer.push(np.asarray([3.0, 7.0, 11.0], dtype=np.float32))
-
-        median = buffer.median()
-
-        self.assertIsNotNone(median)
-        self.assertTrue(np.allclose(median, np.asarray([2.0, 6.0, 10.0], dtype=np.float32)))
-
-    def test_buffer_wraparound_keeps_latest_rows(self):
-        buffer = BackgroundMedianBuffer(max_rows=2)
-        buffer.push(np.asarray([1.0, 5.0], dtype=np.float32))
-        buffer.push(np.asarray([3.0, 7.0], dtype=np.float32))
-        buffer.push(np.asarray([9.0, 11.0], dtype=np.float32))
-
-        median = buffer.median()
-
-        self.assertIsNotNone(median)
-        self.assertTrue(np.allclose(median, np.asarray([6.0, 9.0], dtype=np.float32)))
-
-    def test_buffer_reset_clears_state(self):
-        buffer = BackgroundMedianBuffer(max_rows=2)
-        buffer.push(np.asarray([1.0, 2.0], dtype=np.float32))
-
-        buffer.reset()
-
-        self.assertIsNone(buffer.rows)
-        self.assertIsNone(buffer.median())
-        self.assertEqual(buffer.count, 0)
-        self.assertEqual(buffer.head, 0)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_cli.py

@@ -1,42 +0,0 @@
-from __future__ import annotations
-
-import subprocess
-import sys
-import unittest
-from pathlib import Path
-
-
-ROOT = Path(__file__).resolve().parents[1]
-
-
-def _run(*args: str) -> subprocess.CompletedProcess[str]:
-    return subprocess.run(
-        [sys.executable, *args],
-        cwd=ROOT,
-        text=True,
-        capture_output=True,
-        check=False,
-    )
-
-
-class CliTests(unittest.TestCase):
-    def test_wrapper_help_works(self):
-        proc = _run("RFG_ADC_dataplotter.py", "--help")
-        self.assertEqual(proc.returncode, 0)
-        self.assertIn("usage:", proc.stdout)
-        self.assertIn("--peak_search", proc.stdout)
-
-    def test_module_help_works(self):
-        proc = _run("-m", "rfg_adc_plotter.main", "--help")
-        self.assertEqual(proc.returncode, 0)
-        self.assertIn("usage:", proc.stdout)
-        self.assertIn("--parser_16_bit_x2", proc.stdout)
-
-    def test_backend_mpl_reports_removal(self):
-        proc = _run("-m", "rfg_adc_plotter.main", "/dev/null", "--backend", "mpl")
-        self.assertNotEqual(proc.returncode, 0)
-        self.assertIn("Matplotlib backend removed", proc.stderr)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_processing.py

@@ -1,275 +0,0 @@
-from __future__ import annotations
-
-import os
-import tempfile
-import numpy as np
-import unittest
-
-from rfg_adc_plotter.constants import FFT_LEN, SWEEP_FREQ_MAX_GHZ, SWEEP_FREQ_MIN_GHZ
-from rfg_adc_plotter.gui.pyqtgraph_backend import (
-    apply_working_range,
-    apply_working_range_to_aux_curves,
-    compute_background_subtracted_bscan_levels,
-    resolve_visible_aux_curves,
-)
-from rfg_adc_plotter.processing.calibration import (
-    build_calib_envelope,
-    calibrate_freqs,
-    load_calib_envelope,
-    recalculate_calibration_c,
-    save_calib_envelope,
-)
-from rfg_adc_plotter.processing.background import (
-    load_fft_background,
-    save_fft_background,
-    subtract_fft_background,
-)
-from rfg_adc_plotter.processing.fft import (
-    build_positive_only_centered_ifft_spectrum,
-    build_symmetric_ifft_spectrum,
-    compute_distance_axis,
-    compute_fft_mag_row,
-    compute_fft_row,
-    fft_mag_to_db,
-)
-from rfg_adc_plotter.processing.normalization import (
-    build_calib_envelopes,
-    normalize_by_calib,
-    normalize_by_envelope,
-    resample_envelope,
-)
-from rfg_adc_plotter.processing.peaks import find_peak_width_markers, find_top_peaks_over_ref, rolling_median_ref
-
-
-class ProcessingTests(unittest.TestCase):
-    def test_recalculate_calibration_preserves_requested_edges(self):
-        coeffs = recalculate_calibration_c(np.asarray([0.0, 1.0, 0.025], dtype=np.float64), 3.3, 14.3)
-        y0 = coeffs[0] + coeffs[1] * 3.3 + coeffs[2] * (3.3 ** 2)
-        y1 = coeffs[0] + coeffs[1] * 14.3 + coeffs[2] * (14.3 ** 2)
-        self.assertTrue(np.isclose(y0, 3.3))
-        self.assertTrue(np.isclose(y1, 14.3))
-
-    def test_calibrate_freqs_returns_monotonic_axis_and_same_shape(self):
-        sweep = {"F": np.linspace(3.3, 14.3, 32), "I": np.linspace(-1.0, 1.0, 32)}
-        calibrated = calibrate_freqs(sweep)
-        self.assertEqual(calibrated["F"].shape, (32,))
-        self.assertEqual(calibrated["I"].shape, (32,))
-        self.assertTrue(np.all(np.diff(calibrated["F"]) >= 0.0))
-
-    def test_normalizers_and_envelopes_return_finite_ranges(self):
-        calib = (np.sin(np.linspace(0.0, 4.0 * np.pi, 64)) * 5.0).astype(np.float32)
-        raw = calib * 0.75
-        lower, upper = build_calib_envelopes(calib)
-        self.assertEqual(lower.shape, calib.shape)
-        self.assertEqual(upper.shape, calib.shape)
-        self.assertTrue(np.all(lower <= upper))
-        self.assertTrue(np.all(np.isfinite(upper)))
-        self.assertLess(
-            float(np.mean(np.abs(np.diff(upper, n=2)))),
-            float(np.mean(np.abs(np.diff(calib, n=2)))),
-        )
-
-        simple = normalize_by_calib(raw, calib + 10.0, norm_type="simple")
-        projector = normalize_by_calib(raw, calib, norm_type="projector")
-        self.assertEqual(simple.shape, raw.shape)
-        self.assertEqual(projector.shape, raw.shape)
-        self.assertTrue(np.any(np.isfinite(simple)))
-        self.assertTrue(np.any(np.isfinite(projector)))
-
-    def test_file_calibration_envelope_roundtrip_and_division(self):
-        raw = (np.sin(np.linspace(0.0, 8.0 * np.pi, 128)) * 50.0 + 100.0).astype(np.float32)
-        envelope = build_calib_envelope(raw)
-        normalized = normalize_by_envelope(raw, envelope)
-        resampled = resample_envelope(envelope, 96)
-
-        self.assertEqual(envelope.shape, raw.shape)
-        self.assertEqual(normalized.shape, raw.shape)
-        self.assertEqual(resampled.shape, (96,))
-        self.assertTrue(np.any(np.isfinite(normalized)))
-        self.assertTrue(np.all(np.isfinite(envelope)))
-
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            path = os.path.join(tmp_dir, "calibration_envelope")
-            saved_path = save_calib_envelope(path, envelope)
-            loaded = load_calib_envelope(saved_path)
-            self.assertTrue(saved_path.endswith(".npy"))
-            self.assertTrue(np.allclose(loaded, envelope))
-
-    def test_normalize_by_envelope_adds_small_epsilon_to_zero_denominator(self):
-        raw = np.asarray([1.0, 2.0, 3.0], dtype=np.float32)
-        envelope = np.asarray([0.0, 1.0, -1.0], dtype=np.float32)
-        normalized = normalize_by_envelope(raw, envelope)
-
-        self.assertTrue(np.all(np.isfinite(normalized)))
-        self.assertGreater(normalized[0], 1e8)
-        self.assertAlmostEqual(float(normalized[1]), 2.0, places=5)
-        self.assertAlmostEqual(float(normalized[2]), -3.0, places=5)
-
-    def test_load_calib_envelope_rejects_empty_payload(self):
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            path = os.path.join(tmp_dir, "empty.npy")
-            np.save(path, np.zeros((0,), dtype=np.float32))
-            with self.assertRaises(ValueError):
-                load_calib_envelope(path)
-
-    def test_fft_background_roundtrip_and_rejects_non_1d_payload(self):
-        background = np.asarray([0.5, 1.5, 2.5], dtype=np.float32)
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            path = os.path.join(tmp_dir, "fft_background")
-            saved_path = save_fft_background(path, background)
-            loaded = load_fft_background(saved_path)
-            self.assertTrue(saved_path.endswith(".npy"))
-            self.assertTrue(np.allclose(loaded, background))
-
-            invalid_path = os.path.join(tmp_dir, "fft_background_invalid.npy")
-            np.save(invalid_path, np.zeros((2, 2), dtype=np.float32))
-            with self.assertRaises(ValueError):
-                load_fft_background(invalid_path)
-
-    def test_subtract_fft_background_clamps_negative_residuals_to_zero(self):
-        signal = np.asarray([1.0, 2.0, 3.0], dtype=np.float32)
-        background = np.asarray([1.0, 1.5, 5.0], dtype=np.float32)
-        subtracted = subtract_fft_background(signal, background)
-
-        self.assertTrue(np.allclose(subtracted, np.asarray([0.0, 0.5, 0.0], dtype=np.float32)))
-        self.assertTrue(np.allclose(subtract_fft_background(signal, signal), 0.0))
-
-    def test_apply_working_range_crops_sweep_to_selected_band(self):
-        freqs = np.linspace(3.3, 14.3, 12, dtype=np.float64)
-        sweep = np.arange(12, dtype=np.float32)
-        cropped_freqs, cropped_sweep = apply_working_range(freqs, sweep, 5.0, 9.0)
-
-        self.assertGreater(cropped_freqs.size, 0)
-        self.assertEqual(cropped_freqs.shape, cropped_sweep.shape)
-        self.assertGreaterEqual(float(np.min(cropped_freqs)), 5.0)
-        self.assertLessEqual(float(np.max(cropped_freqs)), 9.0)
-
-    def test_apply_working_range_returns_empty_when_no_points_match(self):
-        freqs = np.linspace(3.3, 14.3, 12, dtype=np.float64)
-        sweep = np.arange(12, dtype=np.float32)
-        cropped_freqs, cropped_sweep = apply_working_range(freqs, sweep, 20.0, 21.0)
-
-        self.assertEqual(cropped_freqs.shape, (0,))
-        self.assertEqual(cropped_sweep.shape, (0,))
-
-    def test_apply_working_range_to_aux_curves_uses_same_mask_as_raw_sweep(self):
-        freqs = np.linspace(3.3, 14.3, 6, dtype=np.float64)
-        sweep = np.asarray([0.0, 1.0, np.nan, 3.0, 4.0, 5.0], dtype=np.float32)
-        aux = (
-            np.asarray([10.0, 11.0, 12.0, 13.0, 14.0, 15.0], dtype=np.float32),
-            np.asarray([20.0, 21.0, 22.0, 23.0, 24.0, 25.0], dtype=np.float32),
-        )
-
-        cropped_freqs, cropped_sweep = apply_working_range(freqs, sweep, 4.0, 12.5)
-        cropped_aux = apply_working_range_to_aux_curves(freqs, sweep, aux, 4.0, 12.5)
-
-        self.assertIsNotNone(cropped_aux)
-        self.assertEqual(cropped_aux[0].shape, cropped_freqs.shape)
-        self.assertEqual(cropped_aux[1].shape, cropped_freqs.shape)
-        self.assertEqual(cropped_aux[0].shape, cropped_sweep.shape)
-        self.assertTrue(np.allclose(cropped_aux[0], np.asarray([11.0, 13.0, 14.0], dtype=np.float32)))
-        self.assertTrue(np.allclose(cropped_aux[1], np.asarray([21.0, 23.0, 24.0], dtype=np.float32)))
-
-    def test_resolve_visible_aux_curves_obeys_checkbox_state(self):
-        aux = (
-            np.asarray([1.0, 2.0], dtype=np.float32),
-            np.asarray([3.0, 4.0], dtype=np.float32),
-        )
-
-        self.assertIsNone(resolve_visible_aux_curves(aux, enabled=False))
-        visible = resolve_visible_aux_curves(aux, enabled=True)
-        self.assertIsNotNone(visible)
-        self.assertTrue(np.allclose(visible[0], aux[0]))
-        self.assertTrue(np.allclose(visible[1], aux[1]))
-
-    def test_background_subtracted_bscan_levels_ignore_zero_floor(self):
-        disp_fft_lin = np.zeros((4, 8), dtype=np.float32)
-        disp_fft_lin[1, 2:6] = np.asarray([0.05, 0.1, 0.5, 2.0], dtype=np.float32)
-        disp_fft_lin[2, 1:6] = np.asarray([0.08, 0.2, 0.7, 3.0, 9.0], dtype=np.float32)
-        disp_fft = fft_mag_to_db(disp_fft_lin)
-
-        levels = compute_background_subtracted_bscan_levels(disp_fft_lin, disp_fft)
-
-        self.assertIsNotNone(levels)
-        positive_vals = disp_fft[disp_fft_lin > 0.0]
-        self.assertAlmostEqual(levels[0], float(np.nanpercentile(positive_vals, 15.0)), places=5)
-        self.assertAlmostEqual(levels[1], float(np.nanpercentile(positive_vals, 99.7)), places=5)
-        zero_floor = disp_fft[disp_fft_lin == 0.0]
-        self.assertLess(float(np.nanmax(zero_floor)), levels[0])
-
-    def test_background_subtracted_bscan_levels_fallback_when_residuals_too_sparse(self):
-        disp_fft_lin = np.zeros((3, 4), dtype=np.float32)
-        disp_fft_lin[1, 2] = 1.0
-        disp_fft = fft_mag_to_db(disp_fft_lin)
-
-        levels = compute_background_subtracted_bscan_levels(disp_fft_lin, disp_fft)
-
-        self.assertIsNone(levels)
-
-    def test_fft_helpers_return_expected_shapes(self):
-        sweep = np.sin(np.linspace(0.0, 4.0 * np.pi, 128)).astype(np.float32)
-        freqs = np.linspace(3.3, 14.3, 128, dtype=np.float64)
-        mag = compute_fft_mag_row(sweep, freqs, 513)
-        row = compute_fft_row(sweep, freqs, 513)
-        axis = compute_distance_axis(freqs, 513)
-        self.assertEqual(mag.shape, (513,))
-        self.assertEqual(row.shape, (513,))
-        self.assertEqual(axis.shape, (513,))
-        self.assertTrue(np.all(np.diff(axis) >= 0.0))
-
-    def test_symmetric_ifft_spectrum_has_zero_gap_and_mirrored_band(self):
-        sweep = np.linspace(1.0, 2.0, 128, dtype=np.float32)
-        freqs = np.linspace(4.0, 10.0, 128, dtype=np.float64)
-        spectrum = build_symmetric_ifft_spectrum(sweep, freqs, fft_len=FFT_LEN)
-
-        self.assertIsNotNone(spectrum)
-        freq_axis = np.linspace(-10.0, 10.0, FFT_LEN, dtype=np.float64)
-        neg_idx_all = np.flatnonzero(freq_axis <= (-4.0))
-        pos_idx_all = np.flatnonzero(freq_axis >= 4.0)
-        band_len = int(min(neg_idx_all.size, pos_idx_all.size))
-        neg_idx = neg_idx_all[:band_len]
-        pos_idx = pos_idx_all[-band_len:]
-        zero_mask = (freq_axis > (-4.0)) & (freq_axis < 4.0)
-
-        self.assertTrue(np.allclose(spectrum[zero_mask], 0.0))
-        self.assertTrue(np.allclose(spectrum[neg_idx], spectrum[pos_idx][::-1]))
-
-    def test_positive_only_centered_spectrum_keeps_zeros_until_positive_min(self):
-        sweep = np.linspace(1.0, 2.0, 128, dtype=np.float32)
-        freqs = np.linspace(4.0, 10.0, 128, dtype=np.float64)
-        spectrum = build_positive_only_centered_ifft_spectrum(sweep, freqs, fft_len=FFT_LEN)
-
-        self.assertIsNotNone(spectrum)
-        freq_axis = np.linspace(-10.0, 10.0, FFT_LEN, dtype=np.float64)
-        zero_mask = freq_axis < 4.0
-        pos_idx = np.flatnonzero(freq_axis >= 4.0)
-
-        self.assertTrue(np.allclose(spectrum[zero_mask], 0.0))
-        self.assertTrue(np.any(np.abs(spectrum[pos_idx]) > 0.0))
-
-    def test_symmetric_distance_axis_uses_windowed_frequency_bounds(self):
-        freqs = np.linspace(4.0, 10.0, 128, dtype=np.float64)
-        axis = compute_distance_axis(freqs, 513, mode="symmetric")
-        df_hz = (2.0 * 10.0 / max(1, FFT_LEN - 1)) * 1e9
-        expected_step = 299_792_458.0 / (2.0 * FFT_LEN * df_hz)
-
-        self.assertEqual(axis.shape, (513,))
-        self.assertTrue(np.all(np.diff(axis) >= 0.0))
-        self.assertAlmostEqual(float(axis[1] - axis[0]), expected_step, places=15)
-
-    def test_peak_helpers_find_reference_and_peak_boxes(self):
-        xs = np.linspace(0.0, 10.0, 200)
-        ys = np.exp(-((xs - 5.0) ** 2) / 0.4) * 10.0 + 1.0
-        ref = rolling_median_ref(xs, ys, 2.0)
-        peaks = find_top_peaks_over_ref(xs, ys, ref, top_n=3)
-        width = find_peak_width_markers(xs, ys)
-        self.assertEqual(ref.shape, ys.shape)
-        self.assertEqual(len(peaks), 1)
-        self.assertGreater(peaks[0]["x"], 4.0)
-        self.assertLess(peaks[0]["x"], 6.0)
-        self.assertIsNotNone(width)
-        self.assertGreater(width["width"], 0.0)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_ring_buffer.py

@@ -1,90 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-import unittest
-
-from rfg_adc_plotter.state.ring_buffer import RingBuffer
-
-
-class RingBufferTests(unittest.TestCase):
-    def test_ring_buffer_initializes_on_first_push(self):
-        ring = RingBuffer(max_sweeps=4)
-        sweep = np.linspace(-1.0, 1.0, 64, dtype=np.float32)
-        ring.push(sweep, np.linspace(3.3, 14.3, 64))
-        self.assertIsNotNone(ring.ring)
-        self.assertIsNotNone(ring.ring_fft)
-        self.assertIsNotNone(ring.ring_time)
-        self.assertIsNotNone(ring.distance_axis)
-        self.assertIsNotNone(ring.get_last_fft_linear())
-        self.assertIsNotNone(ring.last_fft_db)
-        self.assertEqual(ring.ring.shape[0], 4)
-        self.assertEqual(ring.ring_fft.shape, (4, ring.fft_bins))
-
-    def test_ring_buffer_reallocates_when_sweep_width_grows(self):
-        ring = RingBuffer(max_sweeps=3)
-        ring.push(np.ones((32,), dtype=np.float32), np.linspace(3.3, 14.3, 32))
-        first_width = ring.width
-        ring.push(np.ones((2048,), dtype=np.float32), np.linspace(3.3, 14.3, 2048))
-        self.assertGreater(ring.width, first_width)
-        self.assertIsNotNone(ring.ring)
-        self.assertEqual(ring.ring.shape, (3, ring.width))
-
-    def test_ring_buffer_tracks_latest_fft_and_display_arrays(self):
-        ring = RingBuffer(max_sweeps=2)
-        ring.push(np.linspace(0.0, 1.0, 64, dtype=np.float32), np.linspace(3.3, 14.3, 64))
-        ring.push(np.linspace(1.0, 0.0, 64, dtype=np.float32), np.linspace(3.3, 14.3, 64))
-        raw = ring.get_display_raw()
-        fft = ring.get_display_fft_linear()
-        self.assertEqual(raw.shape[1], 2)
-        self.assertEqual(fft.shape[1], 2)
-        self.assertIsNotNone(ring.last_fft_db)
-        self.assertEqual(ring.last_fft_db.shape, (ring.fft_bins,))
-
-    def test_ring_buffer_can_switch_fft_mode_and_rebuild_fft_rows(self):
-        ring = RingBuffer(max_sweeps=2)
-        sweep = np.linspace(0.0, 1.0, 64, dtype=np.float32)
-        freqs = np.linspace(3.3, 14.3, 64, dtype=np.float64)
-        ring.push(sweep, freqs)
-        fft_before = ring.last_fft_db.copy()
-        axis_before = ring.distance_axis.copy()
-
-        changed = ring.set_symmetric_fft_enabled(False)
-
-        self.assertTrue(changed)
-        self.assertFalse(ring.fft_symmetric)
-        self.assertEqual(ring.get_display_raw().shape[1], 2)
-        self.assertIsNotNone(ring.get_last_fft_linear())
-        self.assertEqual(ring.last_fft_db.shape, fft_before.shape)
-        self.assertFalse(np.allclose(ring.last_fft_db, fft_before))
-        self.assertFalse(np.allclose(ring.distance_axis, axis_before))
-
-    def test_ring_buffer_can_switch_to_positive_only_fft_mode(self):
-        ring = RingBuffer(max_sweeps=2)
-        sweep = np.linspace(0.0, 1.0, 64, dtype=np.float32)
-        freqs = np.linspace(3.3, 14.3, 64, dtype=np.float64)
-        ring.push(sweep, freqs)
-
-        changed = ring.set_fft_mode("positive_only")
-
-        self.assertTrue(changed)
-        self.assertEqual(ring.fft_mode, "positive_only")
-        self.assertIsNotNone(ring.last_fft_db)
-        self.assertEqual(ring.last_fft_db.shape, (ring.fft_bins,))
-        self.assertIsNotNone(ring.distance_axis)
-
-    def test_ring_buffer_reset_clears_cached_history(self):
-        ring = RingBuffer(max_sweeps=2)
-        ring.push(np.linspace(0.0, 1.0, 64, dtype=np.float32), np.linspace(4.0, 10.0, 64))
-
-        ring.reset()
-
-        self.assertIsNone(ring.ring)
-        self.assertIsNone(ring.ring_fft)
-        self.assertIsNone(ring.distance_axis)
-        self.assertIsNone(ring.last_fft_db)
-        self.assertEqual(ring.width, 0)
-        self.assertEqual(ring.head, 0)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_sweep_parser_core.py

@@ -1,159 +0,0 @@
-from __future__ import annotations
-
-import math
-import unittest
-
-from rfg_adc_plotter.io.sweep_parser_core import (
-    AsciiSweepParser,
-    LegacyBinaryParser,
-    LogScale16BitX2BinaryParser,
-    LogScaleBinaryParser32,
-    ParserTestStreamParser,
-    PointEvent,
-    StartEvent,
-    SweepAssembler,
-    log_pair_to_sweep,
-)
-
-
-def _u16le(word: int) -> bytes:
-    w = int(word) & 0xFFFF
-    return bytes((w & 0xFF, (w >> 8) & 0xFF))
-
-
-def _pack_legacy_start(ch: int) -> bytes:
-    return b"\xff\xff" * 3 + bytes((0x0A, int(ch) & 0xFF))
-
-
-def _pack_legacy_point(ch: int, step: int, value_i32: int) -> bytes:
-    value = int(value_i32) & 0xFFFF_FFFF
-    return b"".join(
-        [
-            _u16le(step),
-            _u16le((value >> 16) & 0xFFFF),
-            _u16le(value & 0xFFFF),
-            bytes((0x0A, int(ch) & 0xFF)),
-        ]
-    )
-
-
-def _pack_log_start(ch: int) -> bytes:
-    return b"\xff\xff" * 5 + bytes((0x0A, int(ch) & 0xFF))
-
-
-def _pack_log_point(step: int, avg1: int, avg2: int, ch: int = 0) -> bytes:
-    a1 = int(avg1) & 0xFFFF_FFFF
-    a2 = int(avg2) & 0xFFFF_FFFF
-    return b"".join(
-        [
-            _u16le(step),
-            _u16le((a1 >> 16) & 0xFFFF),
-            _u16le(a1 & 0xFFFF),
-            _u16le((a2 >> 16) & 0xFFFF),
-            _u16le(a2 & 0xFFFF),
-            bytes((0x0A, int(ch) & 0xFF)),
-        ]
-    )
-
-
-def _pack_log16_start(ch: int) -> bytes:
-    return b"\xff\xff" * 3 + bytes((0x0A, int(ch) & 0xFF))
-
-
-def _pack_log16_point(step: int, avg1: int, avg2: int) -> bytes:
-    return b"".join(
-        [
-            _u16le(step),
-            _u16le(avg1),
-            _u16le(avg2),
-            _u16le(0xFFFF),
-        ]
-    )
-
-
-class SweepParserCoreTests(unittest.TestCase):
-    def test_ascii_parser_emits_start_and_points(self):
-        parser = AsciiSweepParser()
-        events = parser.feed(b"Sweep_start\ns 1 2 -3\ns2 4 5\n")
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertIsInstance(events[2], PointEvent)
-        self.assertEqual(events[1].ch, 1)
-        self.assertEqual(events[1].x, 2)
-        self.assertEqual(events[1].y, -3.0)
-        self.assertEqual(events[2].ch, 2)
-        self.assertEqual(events[2].x, 4)
-        self.assertEqual(events[2].y, 5.0)
-
-    def test_legacy_binary_parser_resynchronizes_after_garbage(self):
-        parser = LegacyBinaryParser()
-        stream = b"\x00junk" + _pack_legacy_start(3) + _pack_legacy_point(3, 1, -2)
-        events = parser.feed(stream)
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertEqual(events[0].ch, 3)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertEqual(events[1].ch, 3)
-        self.assertEqual(events[1].x, 1)
-        self.assertEqual(events[1].y, -2.0)
-
-    def test_logscale_32_parser_keeps_channel_and_aux_values(self):
-        parser = LogScaleBinaryParser32()
-        stream = _pack_log_start(5) + _pack_log_point(7, 1500, 700, ch=5)
-        events = parser.feed(stream)
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertEqual(events[0].ch, 5)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertEqual(events[1].ch, 5)
-        self.assertEqual(events[1].x, 7)
-        self.assertAlmostEqual(events[1].y, log_pair_to_sweep(1500, 700), places=6)
-        self.assertEqual(events[1].aux, (1500.0, 700.0))
-
-    def test_log_pair_to_sweep_is_order_independent(self):
-        self.assertAlmostEqual(log_pair_to_sweep(1500, 700), log_pair_to_sweep(700, 1500), places=6)
-
-    def test_logscale_16bit_parser_uses_last_start_channel(self):
-        parser = LogScale16BitX2BinaryParser()
-        stream = _pack_log16_start(2) + _pack_log16_point(1, 100, 90)
-        events = parser.feed(stream)
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertEqual(events[0].ch, 2)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertEqual(events[1].ch, 2)
-        self.assertEqual(events[1].aux, (100.0, 90.0))
-
-    def test_parser_test_stream_parser_recovers_point_after_single_separator(self):
-        parser = ParserTestStreamParser()
-        stream = b"".join(
-            [
-                b"\xff\xff\xff\xff",
-                bytes((0x0A, 4)),
-                _u16le(1),
-                _u16le(100),
-                _u16le(90),
-                _u16le(0xFFFF),
-            ]
-        )
-        events = parser.feed(stream)
-        events.extend(parser.feed(_u16le(2)))
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertEqual(events[0].ch, 4)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertEqual(events[1].ch, 4)
-        self.assertEqual(events[1].x, 1)
-        self.assertTrue(math.isfinite(events[1].y))
-
-    def test_sweep_assembler_builds_aux_curves_without_inversion(self):
-        assembler = SweepAssembler(fancy=False, apply_inversion=False)
-        self.assertIsNone(assembler.consume(StartEvent(ch=1)))
-        assembler.consume(PointEvent(ch=1, x=1, y=10.0, aux=(100.0, 90.0)))
-        assembler.consume(PointEvent(ch=1, x=2, y=20.0, aux=(110.0, 95.0)))
-        sweep, info, aux = assembler.finalize_current()
-        self.assertEqual(sweep.shape[0], 3)
-        self.assertEqual(info["ch"], 1)
-        self.assertIsNotNone(aux)
-        self.assertEqual(aux[0][1], 100.0)
-        self.assertEqual(aux[1][2], 95.0)
-
-
-if __name__ == "__main__":
-    unittest.main()